get things going kinda
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818
pdm.lock
818
pdm.lock
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@ -5,11 +5,23 @@
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@ -101,6 +113,27 @@ files = [
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[[package]]
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name = "cloudpickle"
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version = "3.1.0"
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@ -204,6 +237,57 @@ files = [
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summary = "Collection of common python utils"
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[[package]]
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version = "1.11.0"
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"importlib-resources",
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"typing-extensions",
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"typing-extensions",
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"zipp",
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files = [
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[[package]]
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summary = "Collection of common python utils"
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marker = "python_version >= \"3.12\" and python_version < \"3.13\""
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"etils==1.11.0",
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"typing-extensions",
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[[package]]
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name = "executing"
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version = "2.1.0"
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@ -227,6 +311,42 @@ files = [
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{file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
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[[package]]
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summary = "A platform independent file lock."
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name = "flax"
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requires_python = ">=3.10"
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summary = "Flax: A neural network library for JAX designed for flexibility"
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marker = "python_version >= \"3.12\" and python_version < \"3.13\""
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"jax>=0.4.27",
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"optax",
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"orbax-checkpoint",
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"rich>=11.1",
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"tensorstore",
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"typing-extensions>=4.2",
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[[package]]
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name = "fonttools"
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version = "4.55.0"
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@ -247,6 +367,18 @@ files = [
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[[package]]
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name = "gymnasium"
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version = "1.0.0"
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@ -266,6 +398,37 @@ files = [
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{file = "gymnasium-1.0.0.tar.gz", hash = "sha256:9d2b66f30c1b34fe3c2ce7fae65ecf365d0e9982d2b3d860235e773328a3b403"},
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[[package]]
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name = "gymnasium"
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version = "1.0.0"
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extras = ["jax"]
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requires_python = ">=3.8"
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summary = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
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"gymnasium==1.0.0",
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"jax>=0.4.0",
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"jaxlib>=0.4.0",
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[[package]]
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name = "humanize"
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requires_python = ">=3.9"
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summary = "Python humanize utilities"
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||||
[[package]]
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name = "idna"
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version = "3.10"
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@ -278,6 +441,21 @@ files = [
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{file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
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||||
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summary = "Read resources from Python packages"
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||||
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[[package]]
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name = "iniconfig"
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version = "2.0.0"
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@ -404,6 +582,21 @@ files = [
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[[package]]
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summary = "A very fast and expressive template engine."
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||||
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@ -468,6 +661,42 @@ files = [
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||||
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||||
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[[package]]
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@ -512,6 +741,18 @@ files = [
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[[package]]
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name = "mdurl"
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||||
requires_python = ">=3.7"
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||||
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|
||||
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|
||||
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[[package]]
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||||
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@ -538,12 +779,45 @@ files = [
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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[[package]]
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
[[package]]
|
||||
name = "nvidia-nvjitlink-cu12"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
|
||||
[[package]]
|
||||
|
|
@ -603,6 +1034,52 @@ files = [
|
|||
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|
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|
||||
[[package]]
|
||||
name = "optax"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"chex>=0.1.87",
|
||||
"etils[epy]",
|
||||
"jax>=0.4.27",
|
||||
"jaxlib>=0.4.27",
|
||||
"numpy>=1.18.0",
|
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|
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|
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
"etils[epath,epy]",
|
||||
"humanize",
|
||||
"jax>=0.4.34",
|
||||
"msgpack",
|
||||
"nest-asyncio",
|
||||
"numpy",
|
||||
"protobuf",
|
||||
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|
||||
"simplejson>=3.16.0",
|
||||
"tensorstore>=0.1.68",
|
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"typing-extensions",
|
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|
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|
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|
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|
||||
[[package]]
|
||||
name = "packaging"
|
||||
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|
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|
|
@ -615,6 +1092,32 @@ files = [
|
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{file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
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|
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|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"numpy>=1.23.2; python_version == \"3.11\"",
|
||||
"numpy>=1.26.0; python_version >= \"3.12\"",
|
||||
"python-dateutil>=2.8.2",
|
||||
"pytz>=2020.1",
|
||||
"tzdata>=2022.7",
|
||||
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|
||||
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|
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|
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|
||||
{file = "pandas-2.2.3.tar.gz", hash = "sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667"},
|
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|
||||
|
||||
[[package]]
|
||||
name = "parso"
|
||||
version = "0.8.4"
|
||||
|
|
@ -719,6 +1222,23 @@ files = [
|
|||
{file = "prompt_toolkit-3.0.48.tar.gz", hash = "sha256:d6623ab0477a80df74e646bdbc93621143f5caf104206aa29294d53de1a03d90"},
|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
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|
||||
requires_python = ">=3.8"
|
||||
summary = ""
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
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|
||||
{file = "protobuf-5.29.1-cp310-abi3-win32.whl", hash = "sha256:22c1f539024241ee545cbcb00ee160ad1877975690b16656ff87dde107b5f110"},
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "psutil"
|
||||
version = "6.1.0"
|
||||
|
|
@ -776,7 +1296,7 @@ name = "pygments"
|
|||
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|
||||
requires_python = ">=3.8"
|
||||
summary = "Pygments is a syntax highlighting package written in Python."
|
||||
groups = ["dev"]
|
||||
groups = ["default", "dev"]
|
||||
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|
||||
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|
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|
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|
|
@ -899,6 +1419,17 @@ files = [
|
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|
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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{file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "pyvista"
|
||||
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|
||||
|
|
@ -948,6 +1479,26 @@ files = [
|
|||
{file = "pywin32-308-cp312-cp312-win_arm64.whl", hash = "sha256:9b4de86c8d909aed15b7011182c8cab38c8850de36e6afb1f0db22b8959e3091"},
|
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
requires_python = ">=3.8"
|
||||
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|
||||
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|
||||
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|
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|
||||
{file = "PyYAML-6.0.2-cp312-cp312-win32.whl", hash = "sha256:ef6107725bd54b262d6dedcc2af448a266975032bc85ef0172c5f059da6325b4"},
|
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{file = "PyYAML-6.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:7e7401d0de89a9a855c839bc697c079a4af81cf878373abd7dc625847d25cbd8"},
|
||||
{file = "pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pyzmq"
|
||||
version = "26.2.0"
|
||||
|
|
@ -1007,6 +1558,23 @@ files = [
|
|||
{file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rich"
|
||||
version = "13.9.4"
|
||||
requires_python = ">=3.8.0"
|
||||
summary = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
dependencies = [
|
||||
"markdown-it-py>=2.2.0",
|
||||
"pygments<3.0.0,>=2.13.0",
|
||||
"typing-extensions<5.0,>=4.0.0; python_version < \"3.11\"",
|
||||
]
|
||||
files = [
|
||||
{file = "rich-13.9.4-py3-none-any.whl", hash = "sha256:6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90"},
|
||||
{file = "rich-13.9.4.tar.gz", hash = "sha256:439594978a49a09530cff7ebc4b5c7103ef57baf48d5ea3184f21d9a2befa098"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.14.1"
|
||||
|
|
@ -1049,6 +1617,18 @@ files = [
|
|||
{file = "scooby-0.10.0.tar.gz", hash = "sha256:7ea33c262c0cc6a33c6eeeb5648df787be4f22660e53c114e5fff1b811a8854f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "75.6.0"
|
||||
requires_python = ">=3.9"
|
||||
summary = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
files = [
|
||||
{file = "setuptools-75.6.0-py3-none-any.whl", hash = "sha256:ce74b49e8f7110f9bf04883b730f4765b774ef3ef28f722cce7c273d253aaf7d"},
|
||||
{file = "setuptools-75.6.0.tar.gz", hash = "sha256:8199222558df7c86216af4f84c30e9b34a61d8ba19366cc914424cdbd28252f6"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "shiboken6"
|
||||
version = "6.8.0.2"
|
||||
|
|
@ -1063,6 +1643,31 @@ files = [
|
|||
{file = "shiboken6-6.8.0.2-cp39-abi3-win_amd64.whl", hash = "sha256:b11e750e696bb565d897e0f5836710edfb86bd355f87b09988bd31b2aad404d3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "simplejson"
|
||||
version = "3.19.3"
|
||||
requires_python = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.5"
|
||||
summary = "Simple, fast, extensible JSON encoder/decoder for Python"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
files = [
|
||||
{file = "simplejson-3.19.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:66a0399e21c2112acacfebf3d832ebe2884f823b1c7e6d1363f2944f1db31a99"},
|
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{file = "simplejson-3.19.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:6ef9383c5e05f445be60f1735c1816163c874c0b1ede8bb4390aff2ced34f333"},
|
||||
{file = "simplejson-3.19.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:42e5acf80d4d971238d4df97811286a044d720693092b20a56d5e56b7dcc5d09"},
|
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{file = "simplejson-3.19.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d0b0efc7279d768db7c74d3d07f0b5c81280d16ae3fb14e9081dc903e8360771"},
|
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{file = "simplejson-3.19.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0552eb06e7234da892e1d02365cd2b7b2b1f8233aa5aabdb2981587b7cc92ea0"},
|
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{file = "simplejson-3.19.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5bf6a3b9a7d7191471b464fe38f684df10eb491ec9ea454003edb45a011ab187"},
|
||||
{file = "simplejson-3.19.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7017329ca8d4dca94ad5e59f496e5fc77630aecfc39df381ffc1d37fb6b25832"},
|
||||
{file = "simplejson-3.19.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:67a20641afebf4cfbcff50061f07daad1eace6e7b31d7622b6fa2c40d43900ba"},
|
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{file = "simplejson-3.19.3-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:dd6a7dabcc4c32daf601bc45e01b79175dde4b52548becea4f9545b0a4428169"},
|
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{file = "simplejson-3.19.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:08f9b443a94e72dd02c87098c96886d35790e79e46b24e67accafbf13b73d43b"},
|
||||
{file = "simplejson-3.19.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fa97278ae6614346b5ca41a45a911f37a3261b57dbe4a00602048652c862c28b"},
|
||||
{file = "simplejson-3.19.3-cp312-cp312-win32.whl", hash = "sha256:ef28c3b328d29b5e2756903aed888960bc5df39b4c2eab157ae212f70ed5bf74"},
|
||||
{file = "simplejson-3.19.3-cp312-cp312-win_amd64.whl", hash = "sha256:1e662336db50ad665777e6548b5076329a94a0c3d4a0472971c588b3ef27de3a"},
|
||||
{file = "simplejson-3.19.3-py3-none-any.whl", hash = "sha256:49cc4c7b940d43bd12bf87ec63f28cbc4964fc4e12c031cc8cd01650f43eb94e"},
|
||||
{file = "simplejson-3.19.3.tar.gz", hash = "sha256:8e086896c36210ab6050f2f9f095a5f1e03c83fa0e7f296d6cba425411364680"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
version = "1.16.0"
|
||||
|
|
@ -1075,6 +1680,26 @@ files = [
|
|||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "stable-baselines3"
|
||||
version = "2.4.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms."
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
dependencies = [
|
||||
"cloudpickle",
|
||||
"gymnasium<1.1.0,>=0.29.1",
|
||||
"matplotlib",
|
||||
"numpy<2.0,>=1.20",
|
||||
"pandas",
|
||||
"torch>=1.13",
|
||||
]
|
||||
files = [
|
||||
{file = "stable_baselines3-2.4.0-py3-none-any.whl", hash = "sha256:c37c6986d8b3b253e4e52620dc1e4eaeb28b896ee411de289782deab8f83721f"},
|
||||
{file = "stable_baselines3-2.4.0.tar.gz", hash = "sha256:c56f70c10e0a99973130a0ebee83619d0ec3bf1197e0196383276404d2190cc1"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "stack-data"
|
||||
version = "0.6.3"
|
||||
|
|
@ -1091,6 +1716,90 @@ files = [
|
|||
{file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.13.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Computer algebra system (CAS) in Python"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
dependencies = [
|
||||
"mpmath<1.4,>=1.1.0",
|
||||
]
|
||||
files = [
|
||||
{file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
|
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{file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tensorstore"
|
||||
version = "0.1.71"
|
||||
requires_python = ">=3.10"
|
||||
summary = "Read and write large, multi-dimensional arrays"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
dependencies = [
|
||||
"ml-dtypes>=0.3.1",
|
||||
"numpy>=1.22.0",
|
||||
]
|
||||
files = [
|
||||
{file = "tensorstore-0.1.71-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:0bd87899e1c6049b078e785e8b7871e2579202f5b929e89c3c37340965b922bb"},
|
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{file = "tensorstore-0.1.71-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d3a24feb6195f1c222162965c0107c9ff56d322cca23e19f0e66636f6eb80f14"},
|
||||
{file = "tensorstore-0.1.71-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87a97a34b0475ddc7d2afc40e5dd7f8d12522aa81edfbcccb39628cf591454d5"},
|
||||
{file = "tensorstore-0.1.71-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ced5430bcdfa7fcb3a6bdc44733176158cb877b35bdd233cac82e25b4cc94e92"},
|
||||
{file = "tensorstore-0.1.71-cp312-cp312-win_amd64.whl", hash = "sha256:583f0ec143062176ca21fe8dcc3b3b6f94d7f4ea643443b49942d3d1a2fa29b4"},
|
||||
{file = "tensorstore-0.1.71.tar.gz", hash = "sha256:5c37c7b385517b568282a7aedded446216335d0cb41187c93c80b53596c92c96"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "toolz"
|
||||
version = "1.0.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "List processing tools and functional utilities"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
files = [
|
||||
{file = "toolz-1.0.0-py3-none-any.whl", hash = "sha256:292c8f1c4e7516bf9086f8850935c799a874039c8bcf959d47b600e4c44a6236"},
|
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{file = "toolz-1.0.0.tar.gz", hash = "sha256:2c86e3d9a04798ac556793bced838816296a2f085017664e4995cb40a1047a02"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.5.1"
|
||||
requires_python = ">=3.8.0"
|
||||
summary = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
dependencies = [
|
||||
"filelock",
|
||||
"fsspec",
|
||||
"jinja2",
|
||||
"networkx",
|
||||
"nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-nccl-cu12==2.21.5; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"nvidia-nvtx-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
|
||||
"setuptools; python_version >= \"3.12\"",
|
||||
"sympy==1.12.1; python_version == \"3.8\"",
|
||||
"sympy==1.13.1; python_version >= \"3.9\"",
|
||||
"triton==3.1.0; platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\"",
|
||||
"typing-extensions>=4.8.0",
|
||||
]
|
||||
files = [
|
||||
{file = "torch-2.5.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:ed231a4b3a5952177fafb661213d690a72caaad97d5824dd4fc17ab9e15cec03"},
|
||||
{file = "torch-2.5.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:3f4b7f10a247e0dcd7ea97dc2d3bfbfc90302ed36d7f3952b0008d0df264e697"},
|
||||
{file = "torch-2.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:73e58e78f7d220917c5dbfad1a40e09df9929d3b95d25e57d9f8558f84c9a11c"},
|
||||
{file = "torch-2.5.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:8c712df61101964eb11910a846514011f0b6f5920c55dbf567bff8a34163d5b1"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tornado"
|
||||
version = "6.4.2"
|
||||
|
|
@ -1124,6 +1833,19 @@ files = [
|
|||
{file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.1.0"
|
||||
summary = "A language and compiler for custom Deep Learning operations"
|
||||
groups = ["default"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\" and python_version >= \"3.12\""
|
||||
dependencies = [
|
||||
"filelock",
|
||||
]
|
||||
files = [
|
||||
{file = "triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
|
|
@ -1136,6 +1858,18 @@ files = [
|
|||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2024.2"
|
||||
requires_python = ">=2"
|
||||
summary = "Provider of IANA time zone data"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
files = [
|
||||
{file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
|
||||
{file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.3"
|
||||
|
|
@ -1177,3 +1911,15 @@ files = [
|
|||
{file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
|
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{file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.21.0"
|
||||
requires_python = ">=3.9"
|
||||
summary = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
groups = ["default"]
|
||||
marker = "python_version >= \"3.12\" and python_version < \"3.13\""
|
||||
files = [
|
||||
{file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
|
||||
{file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
|
||||
]
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ description = "A solar car racing simulation library and GUI tool"
|
|||
authors = [
|
||||
{name = "saji", email = "saji@saji.dev"},
|
||||
]
|
||||
dependencies = ["pyqtgraph>=0.13.7", "jax>=0.4.35", "pytest>=8.3.3", "pyside6>=6.8.0.2", "matplotlib>=3.9.2", "gymnasium>=1.0.0", "pyvista>=0.44.2", "pyvistaqt>=0.11.1"]
|
||||
dependencies = ["pyqtgraph>=0.13.7", "jax>=0.4.35", "pytest>=8.3.3", "pyside6>=6.8.0.2", "matplotlib>=3.9.2", "gymnasium[jax]>=1.0.0", "pyvista>=0.44.2", "pyvistaqt>=0.11.1", "stable-baselines3>=2.4.0"]
|
||||
requires-python = ">=3.10,<3.13"
|
||||
readme = "README.md"
|
||||
license = {text = "MIT"}
|
||||
|
|
|
|||
|
|
@ -1,34 +1,85 @@
|
|||
|
||||
from typing import Optional
|
||||
import numpy as np
|
||||
import gymnasium as gym
|
||||
from solarcarsim.physsim import CarParams, DefaultCar
|
||||
|
||||
|
||||
|
||||
|
||||
import solarcarsim.physsim as sim
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import numpy as np
|
||||
from typing import Any
|
||||
from functools import partial
|
||||
from jax import vmap
|
||||
|
||||
class SolarRaceV1(gym.Env):
|
||||
""" A primitive hill climber. Aims to solve the given route optimizing
|
||||
for energy usage and on-time arrival. Does not have wind or cloud simulations.
|
||||
Does simulate drag, rolling resistance, and slope power. The action space is the
|
||||
velocity of the car.
|
||||
"""A primitive hill climber. Aims to solve the given route optimizing
|
||||
for energy usage and on-time arrival.
|
||||
"""
|
||||
|
||||
def __init__(self, car: CarParams = DefaultCar(), terrain = None, timestep: float = 1.0):
|
||||
# TODO: terrain parameters
|
||||
|
||||
# car max speed.
|
||||
self.params = {
|
||||
"timestep": timestep,
|
||||
"car": car
|
||||
# these are some simulator helpers
|
||||
def _reset_sim(self, key):
|
||||
self._environment = sim.make_environment(key)
|
||||
# self._state = jnp.array([np.array([x], dtype="float32") for x in (0,0,0, 10000.0, 600.0)])
|
||||
self._state = jnp.array([[0],[0],[0],[10000.0], [600.0]])
|
||||
# self._state = jnp.array([0, 0,0,10000.0, 600.0])
|
||||
|
||||
def _vision_function(self):
|
||||
# extract the vision results.
|
||||
def slookup(x):
|
||||
return jax.lax.dynamic_slice(self._environment[0], x, (100,100))
|
||||
pos = jnp.astype(jnp.round(self._state[0]), "int32")
|
||||
time = jnp.astype(jnp.round(self._state[1]), "int32")
|
||||
wind_view = slookup(jnp.hstack([pos,time]))
|
||||
slope_view = jax.lax.dynamic_slice(self._environment[2], pos, (100,))
|
||||
return slope_view, wind_view
|
||||
|
||||
def _get_obs(self):
|
||||
slope_view, wind_view = self._vision_function()
|
||||
return {
|
||||
"position": self._state[0],
|
||||
"time": self._state[1],
|
||||
"energy": self._state[2],
|
||||
"dist_remaining": self._state[3],
|
||||
"time_remaining": self._state[4],
|
||||
"terrain": slope_view,
|
||||
"wind": wind_view,
|
||||
}
|
||||
|
||||
self.observation_space = gym.spaces.Dict({
|
||||
"position": gym.spaces.Box(0, 1000.0, shape=(1,)),
|
||||
"time": gym.spaces.Box(0,1000),
|
||||
"energy": gym.spaces.Box(-1.0e6, 0.)
|
||||
# TODO: add the elevation profile to the observations.
|
||||
})
|
||||
def __init__(self, car: sim.CarParams = sim.CarParams(), timestep: float = 1.0, seed=1234):
|
||||
|
||||
self.action_space = gym.spaces.Box(0, 5.0, shape=(1,)) #velocity, m/s
|
||||
self._reset_sim(jax.random.key(seed))
|
||||
self._timestep = timestep
|
||||
self._car = car
|
||||
self._simstep = sim.forwardv2
|
||||
self._simreward = sim.reward
|
||||
|
||||
self.observation_space = gym.spaces.Dict(
|
||||
{
|
||||
"position": gym.spaces.Box(-100, 10100.0, shape=(1,)),
|
||||
"time": gym.spaces.Box(0, 1000.0),
|
||||
"energy": gym.spaces.Box(-1.0e6, 0.0),
|
||||
"dist_remaining": gym.spaces.Box(0.0, 10100.0),
|
||||
"time_remaining": gym.spaces.Box(0.0, 600.0),
|
||||
# This is the window into the future/ahead spatially.
|
||||
"terrain": gym.spaces.Box(-1.0, 1.0, shape=(100,)), # slope
|
||||
"wind": gym.spaces.Box(-10.0, 10.0, shape=(100, 100)),
|
||||
}
|
||||
)
|
||||
|
||||
self.action_space = gym.spaces.Box(-1.0, 1.0, shape=(1,)) # velocity, m/s
|
||||
|
||||
|
||||
def reset(self, *, seed = None, options = None):
|
||||
self._reset_sim(jax.random.key(seed or 0))
|
||||
super().reset(seed=seed, options=options)
|
||||
return self._get_obs(), {}
|
||||
|
||||
def step(self, action):
|
||||
wind, elevation, slope = self._environment
|
||||
|
||||
self._state = self._simstep(self._state, action, self._timestep,wind, elevation, slope, self._car)
|
||||
reward = self._simreward(self._state)[0]
|
||||
terminated = False
|
||||
truncated = False
|
||||
if jnp.all(self._state[0] > 10000):
|
||||
terminated = True
|
||||
if self._state[1] > 600:
|
||||
truncated = True
|
||||
|
||||
return self._get_obs(), reward, terminated, truncated, {}
|
||||
0
src/solarcarsim/main.py
Normal file
0
src/solarcarsim/main.py
Normal file
343
src/solarcarsim/noise.py
Normal file
343
src/solarcarsim/noise.py
Normal file
|
|
@ -0,0 +1,343 @@
|
|||
import jax
|
||||
import jax.numpy as jnp
|
||||
from jax import random
|
||||
from functools import partial
|
||||
from typing import Tuple, Optional
|
||||
|
||||
@partial(jax.jit, static_argnums=(1,))
|
||||
def generate_permutation(key, size: int = 256) -> jnp.ndarray:
|
||||
"""Generate a permutation table for Perlin noise."""
|
||||
perm = jnp.arange(size, dtype=jnp.int32)
|
||||
return random.permutation(key, perm)
|
||||
|
||||
@jax.jit
|
||||
def fade(t: jnp.ndarray) -> jnp.ndarray:
|
||||
"""Smoothing function for Perlin noise interpolation."""
|
||||
return t * t * t * (t * (t * 6 - 15) + 10)
|
||||
|
||||
@jax.jit
|
||||
def grad(hash: jnp.ndarray, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
|
||||
"""Calculate gradient for Perlin noise."""
|
||||
h = hash & 7
|
||||
u = jnp.where(h < 4, x, y)
|
||||
v = jnp.where(h < 4, y, x)
|
||||
return jnp.where(h & 1, -u, u) + jnp.where(h & 2, -2.0 * v, 2.0 * v)
|
||||
|
||||
@partial(jax.jit, static_argnums=(1, 2))
|
||||
def perlin_noise_2d(
|
||||
key,
|
||||
width: int,
|
||||
height: int,
|
||||
scale: float = 50.0
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate 2D Perlin noise.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
width: Width of the output noise
|
||||
height: Height of the output noise
|
||||
scale: Scale of the noise pattern
|
||||
|
||||
Returns:
|
||||
2D array of Perlin noise values
|
||||
"""
|
||||
# Generate permutation table
|
||||
p = generate_permutation(key)
|
||||
p = jnp.concatenate([p, p])
|
||||
|
||||
# Create coordinate grid
|
||||
x = jnp.arange(width, dtype=jnp.float32)
|
||||
y = jnp.arange(height, dtype=jnp.float32)
|
||||
X, Y = jnp.meshgrid(x, y)
|
||||
|
||||
# Scale coordinates
|
||||
X = X / scale
|
||||
Y = Y / scale
|
||||
|
||||
# Integer coordinates
|
||||
X_int = jnp.floor(X).astype(jnp.int32) & 255
|
||||
Y_int = jnp.floor(Y).astype(jnp.int32) & 255
|
||||
|
||||
# Fractional coordinates
|
||||
X_frac = X - jnp.floor(X)
|
||||
Y_frac = Y - jnp.floor(Y)
|
||||
|
||||
# Fade factors
|
||||
u = fade(X_frac)
|
||||
v = fade(Y_frac)
|
||||
|
||||
# Hash coordinates
|
||||
A = p[X_int] + Y_int
|
||||
AA = p[A]
|
||||
AB = p[A + 1]
|
||||
B = p[X_int + 1] + Y_int
|
||||
BA = p[B]
|
||||
BB = p[B + 1]
|
||||
|
||||
# Generate gradients
|
||||
g1 = grad(AA, X_frac, Y_frac)
|
||||
g2 = grad(BA, X_frac - 1, Y_frac)
|
||||
g3 = grad(AB, X_frac, Y_frac - 1)
|
||||
g4 = grad(BB, X_frac - 1, Y_frac - 1)
|
||||
|
||||
# Interpolate
|
||||
t1 = g1 + u * (g2 - g1)
|
||||
t2 = g3 + u * (g4 - g3)
|
||||
return t1 + v * (t2 - t1)
|
||||
|
||||
@partial(jax.jit, static_argnums=(1, 2, 3, 4, 5))
|
||||
def fractal_noise_2d(
|
||||
key,
|
||||
width: int,
|
||||
height: int,
|
||||
octaves: int = 6,
|
||||
persistence: float = 0.5,
|
||||
scale: float = 800.0
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate 2D fractal noise (fBm) using Perlin noise.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
width: Width of the output noise
|
||||
height: Height of the output noise
|
||||
octaves: Number of octaves to combine
|
||||
persistence: How much each octave contributes
|
||||
scale: Initial scale of the noise pattern
|
||||
|
||||
Returns:
|
||||
2D array of fractal noise values
|
||||
"""
|
||||
result = jnp.zeros((height, width))
|
||||
max_amplitude = 0.0
|
||||
|
||||
for i in range(octaves):
|
||||
key, subkey = random.split(key)
|
||||
amplitude = persistence ** i
|
||||
frequency = 2 ** i
|
||||
|
||||
octave = perlin_noise_2d(
|
||||
subkey,
|
||||
width,
|
||||
height,
|
||||
scale / frequency
|
||||
)
|
||||
|
||||
result += amplitude * octave
|
||||
max_amplitude += amplitude
|
||||
|
||||
# Normalize
|
||||
return result / max_amplitude
|
||||
|
||||
# Example usage
|
||||
@partial(jax.jit, static_argnums=(1, 2, 3))
|
||||
def generate_noise_texture(
|
||||
key,
|
||||
width: int,
|
||||
height: int,
|
||||
noise_type: str = "perlin"
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate a noise texture using either Perlin or fractal noise.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
width: Width of the texture
|
||||
height: Height of the texture
|
||||
noise_type: Either "perlin" or "fractal"
|
||||
|
||||
Returns:
|
||||
2D array of noise values normalized to [0, 1]
|
||||
"""
|
||||
if noise_type == "perlin":
|
||||
noise = perlin_noise_2d(key, width, height)
|
||||
else:
|
||||
noise = fractal_noise_2d(key, width, height)
|
||||
|
||||
# Normalize to [0, 1]
|
||||
noise = (noise - jnp.min(noise)) / (jnp.max(noise) - jnp.min(noise))
|
||||
return noise
|
||||
|
||||
|
||||
@jax.jit
|
||||
def grad1d(hash: jnp.ndarray, x: jnp.ndarray) -> jnp.ndarray:
|
||||
"""Calculate gradient for 1D Perlin noise."""
|
||||
h = hash & 15
|
||||
grad = 1.0 + (h & 7) # Gradient value 1-8
|
||||
return jnp.where(h & 8, -grad, grad) * x
|
||||
|
||||
@partial(jax.jit, static_argnums=(1,))
|
||||
def perlin_noise_1d(
|
||||
key,
|
||||
length: int,
|
||||
scale: float = 50.0
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate 1D Perlin noise, suitable for elevation profiles.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
length: Length of the output noise array
|
||||
scale: Scale of the noise pattern
|
||||
|
||||
Returns:
|
||||
1D array of Perlin noise values
|
||||
"""
|
||||
# Generate permutation table
|
||||
p = generate_permutation(key)
|
||||
p = jnp.concatenate([p, p])
|
||||
|
||||
# Create coordinate array
|
||||
x = jnp.arange(length, dtype=jnp.float32) / scale
|
||||
|
||||
# Integer and fractional coordinates
|
||||
x0 = jnp.floor(x).astype(jnp.int32) & 255
|
||||
x_frac = x - jnp.floor(x)
|
||||
|
||||
# Fade factor
|
||||
u = fade(x_frac)
|
||||
|
||||
# Hash coordinates
|
||||
A = p[x0]
|
||||
B = p[x0 + 1]
|
||||
|
||||
# Generate gradients and interpolate
|
||||
g1 = grad1d(A, x_frac)
|
||||
g2 = grad1d(B, x_frac - 1)
|
||||
|
||||
return g1 + u * (g2 - g1)
|
||||
|
||||
@partial(jax.jit, static_argnums=(1, 2, 3))
|
||||
def fractal_noise_1d(
|
||||
key,
|
||||
length: int,
|
||||
octaves: int = 6,
|
||||
persistence: float = 0.5,
|
||||
scale: float = 50.0,
|
||||
height_scale: float = 1.0
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate 1D fractal noise (fBm) using Perlin noise, optimized for elevation profiles.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
length: Length of the output noise array
|
||||
octaves: Number of octaves to combine
|
||||
persistence: How much each octave contributes
|
||||
scale: Initial scale of the noise pattern
|
||||
height_scale: Scaling factor for the final height values
|
||||
|
||||
Returns:
|
||||
1D array of fractal noise values
|
||||
"""
|
||||
result = jnp.zeros(length)
|
||||
max_amplitude = 0.0
|
||||
|
||||
for i in range(octaves):
|
||||
key, subkey = random.split(key)
|
||||
amplitude = persistence ** i
|
||||
frequency = 2 ** i
|
||||
|
||||
octave = perlin_noise_1d(
|
||||
subkey,
|
||||
length,
|
||||
scale / frequency
|
||||
)
|
||||
|
||||
result += amplitude * octave
|
||||
max_amplitude += amplitude
|
||||
|
||||
# Normalize and apply height scaling
|
||||
return (result / max_amplitude) * height_scale
|
||||
|
||||
def generate_wind_field(
|
||||
key,
|
||||
length: int,
|
||||
time_steps: int,
|
||||
base_wind: float = 0.0,
|
||||
wind_variation: float = 5.0,
|
||||
spatial_scale: float = 100.0,
|
||||
temporal_scale: float = 50.0,
|
||||
octaves: int = 4,
|
||||
persistence: float = 0.5
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate a 2D wind field that varies smoothly in both space and time.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
length: Spatial length of the wind field
|
||||
time_steps: Number of time steps to generate
|
||||
base_wind: Base wind speed (m/s)
|
||||
wind_variation: Maximum variation in wind speed (m/s)
|
||||
spatial_scale: Scale of spatial variations
|
||||
temporal_scale: Scale of temporal variations
|
||||
octaves: Number of octaves for fractal noise
|
||||
persistence: Persistence for fractal noise
|
||||
|
||||
Returns:
|
||||
2D array of wind speeds (time_steps × length)
|
||||
"""
|
||||
# Generate 2D noise field
|
||||
noise = fractal_noise_2d(
|
||||
key,
|
||||
length,
|
||||
time_steps,
|
||||
octaves,
|
||||
persistence,
|
||||
spatial_scale
|
||||
)
|
||||
|
||||
# Scale noise to wind speeds
|
||||
wind = base_wind + (noise - 0.5) * 2 * wind_variation
|
||||
|
||||
# Add temporal coherence by applying smoothing in time dimension
|
||||
# This ensures wind changes gradually over time
|
||||
temporal_kernel = jnp.exp(-jnp.arange(5)**2 / (2 * (temporal_scale/100)**2))
|
||||
temporal_kernel = temporal_kernel / jnp.sum(temporal_kernel)
|
||||
wind = jax.scipy.signal.convolve(
|
||||
wind,
|
||||
temporal_kernel[:, jnp.newaxis],
|
||||
mode='same'
|
||||
)
|
||||
|
||||
return wind
|
||||
|
||||
def generate_elevation_profile(
|
||||
key,
|
||||
length: int,
|
||||
base_height: float = 100.0,
|
||||
height_variation: float = 50.0,
|
||||
octaves: int = 6,
|
||||
persistence: float = 0.5,
|
||||
scale: float = 50.0
|
||||
) -> jnp.ndarray:
|
||||
"""
|
||||
Generate a realistic elevation profile using fractal noise.
|
||||
|
||||
Args:
|
||||
key: JAX random key
|
||||
length: Length of the elevation profile
|
||||
base_height: Base elevation level
|
||||
height_variation: Maximum variation in height
|
||||
octaves: Number of octaves for fractal noise
|
||||
persistence: Persistence for fractal noise
|
||||
scale: Scale of the noise pattern
|
||||
|
||||
Returns:
|
||||
1D array of elevation values
|
||||
"""
|
||||
# Generate base terrain using fractal noise
|
||||
noise = fractal_noise_1d(
|
||||
key,
|
||||
length,
|
||||
octaves,
|
||||
persistence,
|
||||
scale,
|
||||
height_variation
|
||||
)
|
||||
|
||||
# Add base height and ensure non-negative elevation
|
||||
elevation = base_height + noise
|
||||
return jnp.maximum(elevation, 0.0)
|
||||
|
|
@ -1,9 +1,12 @@
|
|||
import jax.numpy as jnp
|
||||
import jax
|
||||
from jax import grad, jit, vmap, lax
|
||||
from functools import partial
|
||||
|
||||
from typing import NamedTuple, Tuple
|
||||
|
||||
from solarcarsim.noise import fractal_noise_1d, generate_elevation_profile, generate_wind_field
|
||||
|
||||
class MotorParams(NamedTuple):
|
||||
kv: float
|
||||
kt: float
|
||||
|
|
@ -21,9 +24,11 @@ class CarParams(NamedTuple):
|
|||
""" Physical Data for Solar Car Parameters """
|
||||
mass: float = 800 # kg
|
||||
frontal_area: float = 1.3 # m^2
|
||||
drag_coeff: float = 0.018 # drag coefficient, dimensionless
|
||||
drag_coeff: float = 0.18 # drag coefficient, dimensionless
|
||||
rolling_coeff: float = 0.002 # rolling resistance.
|
||||
moter_eff: float = 0.93 # 0 < x < 1 scaling factor
|
||||
wheel_radius: float = 0.23 # wheel radius in meters
|
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max_speed: float = 30.0 # m/s top speed
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solar_area: float = 5.0 # m^2, typically 5.0
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solar_eff: float = 0.20 # 0 < x < 1, typically ~.25
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n_motors: int = 2 # how many motors we have.
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|
|
@ -59,9 +64,9 @@ def drag_force(u, area, cd, rho):
|
|||
|
||||
# we can use those forces above to determine what forces we have to overcome. Sum(F)=0
|
||||
|
||||
@partial(jit, static_argnums=(3,4,5,6,7,))
|
||||
def bldc_power_draw(torque, velocity, resistance=0.1, kt=0.1,
|
||||
Cf=0.01, iron_loss_coeff=0.005):
|
||||
# @partial(jit, static_argnums=(2,))
|
||||
@jit
|
||||
def bldc_power_draw(torque, velocity, params: MotorParams):
|
||||
"""
|
||||
Approximates power draw of a BLDC motor outputting a torque at a given velocity
|
||||
|
||||
|
|
@ -78,14 +83,14 @@ def bldc_power_draw(torque, velocity, resistance=0.1, kt=0.1,
|
|||
"""
|
||||
|
||||
# Current required for torque (simplified relationship)
|
||||
current = torque / kt
|
||||
current = torque / params.kt
|
||||
|
||||
# Copper losses (I²R)
|
||||
copper_losses = resistance * current**2
|
||||
copper_losses = params.resistance * current**2
|
||||
# Mechanical friction losses
|
||||
friction_losses = Cf * velocity**2
|
||||
friction_losses = params.friction_coeff * velocity**2
|
||||
# Iron losses (simplified model - primarily dependent on speed)
|
||||
iron_losses = iron_loss_coeff * velocity**2
|
||||
iron_losses = params.iron_coeff * velocity**2
|
||||
# Mechanical power output
|
||||
mechanical_power = torque * velocity
|
||||
|
||||
|
|
@ -94,7 +99,8 @@ def bldc_power_draw(torque, velocity, resistance=0.1, kt=0.1,
|
|||
|
||||
return total_power
|
||||
|
||||
@partial(jit, static_argnames=['resistance', 'kt', 'kv', 'vmax', 'Cf'])
|
||||
# @partial(jit, static_argnames=['resistance', 'kt', 'kv', 'vmax', 'Cf'])
|
||||
@jit
|
||||
def bldc_torque(velocity, current_limit, resistance, kt, kv, vmax, Cf):
|
||||
|
||||
bemf = velocity / kv
|
||||
|
|
@ -107,7 +113,7 @@ def bldc_torque(velocity, current_limit, resistance, kt, kv, vmax, Cf):
|
|||
stall_torque = kt * current_limit
|
||||
return jnp.where(velocity < 0.01, stall_torque, net_torque)
|
||||
|
||||
@partial(jit, static_argnums=(2,3,))
|
||||
@partial(jit, static_argnums=(1,2,))
|
||||
def battery_powerloss(current,cell_r, battery_shape: Tuple[int,int]):
|
||||
r_array = jnp.full(battery_shape, cell_r)
|
||||
branch_current = current / battery_shape[1]
|
||||
|
|
@ -118,16 +124,83 @@ def battery_powerloss(current,cell_r, battery_shape: Tuple[int,int]):
|
|||
|
||||
|
||||
def forward(state, timestep, control, params: CarParams):
|
||||
# state is (position, time, velocity, energy)
|
||||
# control is -1 to 1 (motor max current percent.)
|
||||
# state is (position, time, energy)
|
||||
# control is velocity
|
||||
# timestep is >0 time to advance
|
||||
# params is the params dictionary.
|
||||
# returns the next state with (position', time + timestep, velocity', energy')
|
||||
# returns the next state with (position', time + timestep, energy')
|
||||
# TODO: terrain, weather, solar
|
||||
|
||||
# determine the forces acting on the car.
|
||||
dragf = drag_force(state[2], params.frontal_area, params.drag_coeff, 1.184)
|
||||
dragf = drag_force(control, params.frontal_area, params.drag_coeff, 1.184)
|
||||
rollf = rolling_force(params.mass, 0, params.rolling_coeff)
|
||||
hillforce = downslope_force(params.mass, 0)
|
||||
totalf = dragf + rollf + hillforce
|
||||
# determine the power needed to make this force
|
||||
tau = params.wheel_radius * totalf
|
||||
pdraw = bldc_power_draw(tau, control, params.motor)
|
||||
net_power = 0 - pdraw # watts aka j/s
|
||||
|
||||
# TODO: calculate battery-based power losses.
|
||||
# TODO: support regenerative braking when going downhill
|
||||
# TODO: delta x = cos(theta) * velocity * timestep
|
||||
|
||||
new_state = jnp.array([state[0] + control * timestep, state[1] + timestep, state[2] + net_power * timestep])
|
||||
return new_state
|
||||
|
||||
|
||||
def make_environment(seed):
|
||||
""" Generate a race environment: terrain function, wind function, wrapped forward function."""
|
||||
key, subkey = jax.random.split(seed)
|
||||
wind = generate_wind_field(subkey, 10000, 600, spatial_scale=1000)
|
||||
key, subkey = jax.random.split(key)
|
||||
slope = fractal_noise_1d(subkey, 10000, scale=1200, height_scale=0.08)
|
||||
elevation = jnp.cumsum(slope)
|
||||
# elevation = generate_elevation_profile(subkey, 10000, height_variation=40.0, scale=1200, octaves=5)
|
||||
# slope = jnp.arctan(jnp.diff(elevation, prepend=100.0)) # rise/run
|
||||
|
||||
return wind, elevation, slope
|
||||
|
||||
@partial(jit, static_argnames=['params'])
|
||||
def forwardv2(state, control, delta_time, wind, elevation, slope, params):
|
||||
pos = jnp.astype(jnp.round(state[0]), "int32")
|
||||
time = jnp.astype(jnp.round(state[1]), "int32")
|
||||
theta = slope[pos]
|
||||
|
||||
velocity = control * params.max_speed
|
||||
|
||||
# sum up the forces acting on the car
|
||||
dragf = drag_force(velocity, params.frontal_area, params.drag_coeff, 1.184)
|
||||
rollf = rolling_force(params.mass, theta, params.rolling_coeff)
|
||||
hillforce = downslope_force(params.mass, theta)
|
||||
windf = wind[pos, time]
|
||||
totalf = dragf + rollf + hillforce + windf
|
||||
# with the sum of forces, determine the needed torque at the wheels, and then power
|
||||
tau = params.wheel_radius * totalf
|
||||
pdraw = bldc_power_draw(tau, velocity, params.motor)
|
||||
# determine the energy needed to do this power for the time step
|
||||
net_power = state[2] - delta_time * pdraw # joules
|
||||
|
||||
dpos = jnp.cos(theta) * velocity * delta_time
|
||||
dist_remaining = 10000.0 - dpos
|
||||
time_remaining = 600 - (state[1] + delta_time)
|
||||
return jnp.array([dpos, state[1] + delta_time, net_power, dist_remaining, time_remaining])
|
||||
|
||||
def reward(state):
|
||||
progress = state[0] / 10000 * 100
|
||||
energy_usage = -10 * state[2]
|
||||
time_factor = (1.0 - (state[1] / 600)) * 50
|
||||
reward = progress + energy_usage + time_factor
|
||||
return reward
|
||||
# now we have an environment tuned in.
|
||||
# we want to take an environment, and bind it to the forward function
|
||||
def make_simulator(params: CarParams, wind, elevation, slope):
|
||||
def reward(state):
|
||||
progress = state[0] / 10000 * 100
|
||||
energy_usage = -10 * state[2]
|
||||
time_factor = (1.0 - (state[1] / 600)) * 50
|
||||
reward = progress + energy_usage + time_factor
|
||||
return reward
|
||||
return forwardv2, reward
|
||||
|
||||
|
||||
pass
|
||||
|
|
|
|||
Loading…
Reference in a new issue