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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array([ 0.0000000e+00, -2.5544850e-07, -1.4012958e-06, ...,\n",
" -1.1142221e-02, -1.1067827e-02, -1.1001030e-02], dtype=float32)"
]
},
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"execution_count": 1,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"from solarcarsim.physsim import CarParams, fractal_noise_1d\n",
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"from solarcarsim.simv2 import Snax, SimParams\n",
"import chex\n",
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"\n",
"\n",
"key = jax.random.key(0)\n",
"\n",
"slope = fractal_noise_1d(key, 10000, scale=1200, height_scale=0.08)\n",
"\n",
"slope"
]
},
{
"cell_type": "code",
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"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# make a simple triangle-type hill. Slope is 0.2 and then -0.2\n",
"dist = 1000 # it's 1 km long\n",
"xcoords = jnp.arange(dist)\n",
"slope = jnp.concat([jnp.full(int(dist/2), 0.2), jnp.full(int(dist/2), -0.2)])\n",
"def lerp(x):\n",
" return jnp.interp(x, xcoords, slope)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"time_scale = 0.1\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
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"import jax\n",
"import jax.numpy as jnp\n",
"import flax.linen as nn\n",
"import numpy as np\n",
"import optax\n",
"from flax.linen.initializers import constant, orthogonal\n",
"from typing import Sequence, NamedTuple, Any\n",
"from flax.training.train_state import TrainState\n",
"import distrax\n",
"from gymnax.wrappers.purerl import LogWrapper, FlattenObservationWrapper\n",
"\n",
"\n",
"class GymnaxWrapper(object):\n",
" \"\"\"Base class for Gymnax wrappers.\"\"\"\n",
"\n",
" def __init__(self, env):\n",
" self._env = env\n",
"\n",
" # provide proxy access to regular attributes of wrapped object\n",
" def __getattr__(self, name):\n",
" return getattr(self._env, name)\n",
"\n",
"\n",
"class VecEnv(GymnaxWrapper):\n",
" def __init__(self, env):\n",
" super().__init__(env)\n",
" self.reset = jax.vmap(self._env.reset, in_axes=(0, None))\n",
" self.step = jax.vmap(self._env.step, in_axes=(0, 0, 0, None))\n",
"\n",
"class ClipAction(GymnaxWrapper):\n",
" def __init__(self, env, low=-1.0, high=1.0):\n",
" super().__init__(env)\n",
" self.low = low\n",
" self.high = high\n",
"\n",
" def step(self, key, state, action, params=None):\n",
" \"\"\"TODO: In theory the below line should be the way to do this.\"\"\"\n",
" # action = jnp.clip(action, self.env.action_space.low, self.env.action_space.high)\n",
" action = jnp.clip(action, self.low, self.high)\n",
" return self._env.step(key, state, action, params)\n",
"\n",
"\n",
"\n",
"class ActorCritic(nn.Module):\n",
" action_dim: Sequence[int]\n",
" activation: str = \"tanh\"\n",
"\n",
" @nn.compact\n",
" def __call__(self, x):\n",
" if self.activation == \"relu\":\n",
" activation = nn.relu\n",
" else:\n",
" activation = nn.tanh\n",
" actor_mean = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(x)\n",
" actor_mean = activation(actor_mean)\n",
" actor_mean = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(actor_mean)\n",
" actor_mean = activation(actor_mean)\n",
" actor_mean = nn.Dense(\n",
" self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0)\n",
" )(actor_mean)\n",
" actor_logtstd = self.param(\"log_std\", nn.initializers.zeros, (self.action_dim,))\n",
" pi = distrax.MultivariateNormalDiag(actor_mean, jnp.exp(actor_logtstd))\n",
"\n",
" critic = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(x)\n",
" critic = activation(critic)\n",
" critic = nn.Dense(\n",
" 256, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0)\n",
" )(critic)\n",
" critic = activation(critic)\n",
" critic = nn.Dense(1, kernel_init=orthogonal(1.0), bias_init=constant(0.0))(\n",
" critic\n",
" )\n",
"\n",
" return pi, jnp.squeeze(critic, axis=-1)\n",
"\n",
"\n",
"class Transition(NamedTuple):\n",
" done: jnp.ndarray\n",
" action: jnp.ndarray\n",
" value: jnp.ndarray\n",
" reward: jnp.ndarray\n",
" log_prob: jnp.ndarray\n",
" obs: jnp.ndarray\n",
" info: jnp.ndarray\n",
"\n",
"\n",
"def make_train(config):\n",
" config[\"NUM_UPDATES\"] = (\n",
" config[\"TOTAL_TIMESTEPS\"] // config[\"NUM_STEPS\"] // config[\"NUM_ENVS\"]\n",
" )\n",
" config[\"MINIBATCH_SIZE\"] = (\n",
" config[\"NUM_ENVS\"] * config[\"NUM_STEPS\"] // config[\"NUM_MINIBATCHES\"]\n",
" )\n",
" env = Snax()\n",
" env_params = env.default_params\n",
" env = LogWrapper(env)\n",
" env = VecEnv(env)\n",
" #env = ClipAction(env)\n",
" # if config[\"NORMALIZE_ENV\"]:\n",
" # env = NormalizeVecObservation(env)\n",
" # env = NormalizeVecReward(env, config[\"GAMMA\"])\n",
"\n",
" def linear_schedule(count):\n",
" frac = (\n",
" 1.0\n",
" - (count // (config[\"NUM_MINIBATCHES\"] * config[\"UPDATE_EPOCHS\"]))\n",
" / config[\"NUM_UPDATES\"]\n",
" )\n",
" return config[\"LR\"] * frac\n",
"\n",
" def train(rng):\n",
" # INIT NETWORK\n",
" network = ActorCritic(\n",
" env.action_space(env_params).shape[0], activation=config[\"ACTIVATION\"]\n",
" )\n",
" rng, _rng = jax.random.split(rng)\n",
" init_x = jnp.zeros(env.observation_space(env_params).shape)\n",
" network_params = network.init(_rng, init_x)\n",
" if config[\"ANNEAL_LR\"]:\n",
" tx = optax.chain(\n",
" optax.clip_by_global_norm(config[\"MAX_GRAD_NORM\"]),\n",
" optax.adam(learning_rate=linear_schedule, eps=1e-5),\n",
" )\n",
" else:\n",
" tx = optax.chain(\n",
" optax.clip_by_global_norm(config[\"MAX_GRAD_NORM\"]),\n",
" optax.adam(config[\"LR\"], eps=1e-5),\n",
" )\n",
" train_state = TrainState.create(\n",
" apply_fn=network.apply,\n",
" params=network_params,\n",
" tx=tx,\n",
" )\n",
"\n",
" # INIT ENV\n",
" rng, _rng = jax.random.split(rng)\n",
" reset_rng = jax.random.split(_rng, config[\"NUM_ENVS\"])\n",
" obsv, env_state = env.reset(reset_rng, env_params)\n",
"\n",
" # TRAIN LOOP\n",
" def _update_step(runner_state, unused):\n",
" # COLLECT TRAJECTORIES\n",
" def _env_step(runner_state, unused):\n",
" train_state, env_state, last_obs, rng = runner_state\n",
"\n",
" # SELECT ACTION\n",
" rng, _rng = jax.random.split(rng)\n",
" pi, value = network.apply(train_state.params, last_obs)\n",
" action = pi.sample(seed=_rng)\n",
" log_prob = pi.log_prob(action)\n",
"\n",
" # STEP ENV\n",
" rng, _rng = jax.random.split(rng)\n",
" rng_step = jax.random.split(_rng, config[\"NUM_ENVS\"])\n",
" obsv, env_state, reward, done, info = env.step(\n",
" rng_step, env_state, action, env_params\n",
" )\n",
" transition = Transition(\n",
" done, action, value, reward, log_prob, last_obs, info\n",
" )\n",
" runner_state = (train_state, env_state, obsv, rng)\n",
" return runner_state, transition\n",
"\n",
" runner_state, traj_batch = jax.lax.scan(\n",
" _env_step, runner_state, None, config[\"NUM_STEPS\"]\n",
" )\n",
"\n",
" # CALCULATE ADVANTAGE\n",
" train_state, env_state, last_obs, rng = runner_state\n",
" _, last_val = network.apply(train_state.params, last_obs)\n",
"\n",
" def _calculate_gae(traj_batch, last_val):\n",
" def _get_advantages(gae_and_next_value, transition):\n",
" gae, next_value = gae_and_next_value\n",
" done, value, reward = (\n",
" transition.done,\n",
" transition.value,\n",
" transition.reward,\n",
" )\n",
" delta = reward + config[\"GAMMA\"] * next_value * (1 - done) - value\n",
" gae = (\n",
" delta\n",
" + config[\"GAMMA\"] * config[\"GAE_LAMBDA\"] * (1 - done) * gae\n",
" )\n",
" return (gae, value), gae\n",
"\n",
" _, advantages = jax.lax.scan(\n",
" _get_advantages,\n",
" (jnp.zeros_like(last_val), last_val),\n",
" traj_batch,\n",
" reverse=True,\n",
" unroll=16,\n",
" )\n",
" return advantages, advantages + traj_batch.value\n",
"\n",
" advantages, targets = _calculate_gae(traj_batch, last_val)\n",
"\n",
" # UPDATE NETWORK\n",
" def _update_epoch(update_state, unused):\n",
" def _update_minbatch(train_state, batch_info):\n",
" traj_batch, advantages, targets = batch_info\n",
"\n",
" def _loss_fn(params, traj_batch, gae, targets):\n",
" # RERUN NETWORK\n",
" pi, value = network.apply(params, traj_batch.obs)\n",
" log_prob = pi.log_prob(traj_batch.action)\n",
"\n",
" # CALCULATE VALUE LOSS\n",
" value_pred_clipped = traj_batch.value + (\n",
" value - traj_batch.value\n",
" ).clip(-config[\"CLIP_EPS\"], config[\"CLIP_EPS\"])\n",
" value_losses = jnp.square(value - targets)\n",
" value_losses_clipped = jnp.square(value_pred_clipped - targets)\n",
" value_loss = (\n",
" 0.5 * jnp.maximum(value_losses, value_losses_clipped).mean()\n",
" )\n",
"\n",
" # CALCULATE ACTOR LOSS\n",
" ratio = jnp.exp(log_prob - traj_batch.log_prob)\n",
" gae = (gae - gae.mean()) / (gae.std() + 1e-8)\n",
" loss_actor1 = ratio * gae\n",
" loss_actor2 = (\n",
" jnp.clip(\n",
" ratio,\n",
" 1.0 - config[\"CLIP_EPS\"],\n",
" 1.0 + config[\"CLIP_EPS\"],\n",
" )\n",
" * gae\n",
" )\n",
" loss_actor = -jnp.minimum(loss_actor1, loss_actor2)\n",
" loss_actor = loss_actor.mean()\n",
" entropy = pi.entropy().mean()\n",
"\n",
" total_loss = (\n",
" loss_actor\n",
" + config[\"VF_COEF\"] * value_loss\n",
" - config[\"ENT_COEF\"] * entropy\n",
" )\n",
" return total_loss, (value_loss, loss_actor, entropy)\n",
"\n",
" grad_fn = jax.value_and_grad(_loss_fn, has_aux=True)\n",
" total_loss, grads = grad_fn(\n",
" train_state.params, traj_batch, advantages, targets\n",
" )\n",
" train_state = train_state.apply_gradients(grads=grads)\n",
" return train_state, total_loss\n",
"\n",
" train_state, traj_batch, advantages, targets, rng = update_state\n",
" rng, _rng = jax.random.split(rng)\n",
" batch_size = config[\"MINIBATCH_SIZE\"] * config[\"NUM_MINIBATCHES\"]\n",
" assert (\n",
" batch_size == config[\"NUM_STEPS\"] * config[\"NUM_ENVS\"]\n",
" ), \"batch size must be equal to number of steps * number of envs\"\n",
" permutation = jax.random.permutation(_rng, batch_size)\n",
" batch = (traj_batch, advantages, targets)\n",
" batch = jax.tree_util.tree_map(\n",
" lambda x: x.reshape((batch_size,) + x.shape[2:]), batch\n",
" )\n",
" shuffled_batch = jax.tree_util.tree_map(\n",
" lambda x: jnp.take(x, permutation, axis=0), batch\n",
" )\n",
" minibatches = jax.tree_util.tree_map(\n",
" lambda x: jnp.reshape(\n",
" x, [config[\"NUM_MINIBATCHES\"], -1] + list(x.shape[1:])\n",
" ),\n",
" shuffled_batch,\n",
" )\n",
" train_state, total_loss = jax.lax.scan(\n",
" _update_minbatch, train_state, minibatches\n",
" )\n",
" update_state = (train_state, traj_batch, advantages, targets, rng)\n",
" return update_state, total_loss\n",
"\n",
" update_state = (train_state, traj_batch, advantages, targets, rng)\n",
" update_state, loss_info = jax.lax.scan(\n",
" _update_epoch, update_state, None, config[\"UPDATE_EPOCHS\"]\n",
" )\n",
" train_state = update_state[0]\n",
" metric = traj_batch.info\n",
" rng = update_state[-1]\n",
" if config.get(\"DEBUG\"):\n",
"\n",
" def callback(info):\n",
" return_values = info[\"returned_episode_returns\"][\n",
" info[\"returned_episode\"]\n",
" ]\n",
" timesteps = (\n",
" info[\"timestep\"][info[\"returned_episode\"]] * config[\"NUM_ENVS\"]\n",
" )\n",
" for t in range(len(timesteps)):\n",
" print(\n",
" f\"global step={timesteps[t]}, episodic return={return_values[t]}\"\n",
" )\n",
"\n",
" jax.debug.callback(callback, metric)\n",
"\n",
" runner_state = (train_state, env_state, last_obs, rng)\n",
" return runner_state, metric\n",
"\n",
" rng, _rng = jax.random.split(rng)\n",
" runner_state = (train_state, env_state, obsv, _rng)\n",
" metrics = []\n",
" for i in range(config[\"NUM_MAINLOOPS\"]):\n",
" runner_state, metric = jax.lax.scan(\n",
" _update_step, runner_state, None, config[\"NUM_UPDATES\"]\n",
" )\n",
" metrics.append(metric)\n",
" return {\"runner_state\": runner_state, \"metrics\": metrics}\n",
"\n",
" return train\n"
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]
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},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"config = {\n",
" \"LR\": 3e-4,\n",
" \"NUM_ENVS\": 2048,\n",
" \"NUM_STEPS\": 10,\n",
" \"TOTAL_TIMESTEPS\": 5e7,\n",
" \"UPDATE_EPOCHS\": 4,\n",
" \"NUM_MINIBATCHES\": 64,\n",
" \"GAMMA\": 0.99,\n",
" \"GAE_LAMBDA\": 0.95,\n",
" \"CLIP_EPS\": 0.2,\n",
" \"ENT_COEF\": 0.0,\n",
" \"VF_COEF\": 0.5,\n",
" \"MAX_GRAD_NORM\": 0.5,\n",
" \"ACTIVATION\": \"tanh\",\n",
" \"ENV_NAME\": \"hopper\",\n",
" \"ANNEAL_LR\": False,\n",
" \"NORMALIZE_ENV\": True,\n",
" \"DEBUG\": False,\n",
" \"NUM_MAINLOOPS\": 3,\n",
"}\n",
"config_light = {\n",
" \"LR\": 3e-4,\n",
" \"NUM_ENVS\": 128,\n",
" \"NUM_STEPS\": 10,\n",
" \"TOTAL_TIMESTEPS\": 5e3,\n",
" \"UPDATE_EPOCHS\": 2,\n",
" \"NUM_MINIBATCHES\": 16,\n",
" \"GAMMA\": 0.99,\n",
" \"GAE_LAMBDA\": 0.95,\n",
" \"CLIP_EPS\": 0.2,\n",
" \"ENT_COEF\": 0.0,\n",
" \"VF_COEF\": 0.5,\n",
" \"MAX_GRAD_NORM\": 0.5,\n",
" \"ACTIVATION\": \"tanh\",\n",
" \"ENV_NAME\": \"hopper\",\n",
" \"ANNEAL_LR\": False,\n",
" \"NORMALIZE_ENV\": True,\n",
" \"DEBUG\": False,\n",
"}\n",
"\n",
"rng = jax.random.key(42)\n",
"train_jit = jax.jit(make_train(config))\n",
"out = train_jit(rng)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"data = jnp.concatenate([out[\"metrics\"][x][\"returned_episode_returns\"].mean(-1).reshape(-1) for x in range(3)])\n",
"plt.plot(data)\n",
"ax = plt.gca()\n",
"\n",
"plt.xlabel(\"Update Step\")\n",
"plt.ylabel(\"Return\")\n",
"plt.title(\"PPO Agent Returns\")\n",
"plt.savefig(\"PPO_results.pdf\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-12-17 20:40:06.756483: W external/xla/xla/tsl/framework/bfc_allocator.cc:497] Allocator (GPU_0_bfc) ran out of memory trying to allocate 7.63GiB (rounded to 8192000000)requested by op \n",
"2024-12-17 20:40:06.756558: W external/xla/xla/tsl/framework/bfc_allocator.cc:508] ***************************************************_________________________________________________\n",
"E1217 20:40:06.756588 512196 pjrt_stream_executor_client.cc:3086] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 8192000000 bytes.\n"
]
},
{
"ename": "XlaRuntimeError",
"evalue": "RESOURCE_EXHAUSTED: Out of memory while trying to allocate 8192000000 bytes.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mXlaRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[19], line 33\u001b[0m\n\u001b[1;32m 28\u001b[0m runner_state \u001b[38;5;241m=\u001b[39m (train_state, env_state, obsv, rng)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m runner_state, env_state\n\u001b[0;32m---> 33\u001b[0m runner_state, env_logs \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43m_env_step\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrunner_state\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\n\u001b[1;32m 35\u001b[0m \u001b[43m)\u001b[49m\n",
" \u001b[0;31m[... skipping hidden 11 frame]\u001b[0m\n",
"File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/jax/_src/interpreters/pxla.py:1298\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_token_bufs(result_token_bufs, sharded_runtime_token)\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1298\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mneeds_check_special():\n\u001b[1;32m 1301\u001b[0m out_arrays \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_into_single_device_arrays()\n",
"\u001b[0;31mXlaRuntimeError\u001b[0m: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 8192000000 bytes."
]
}
],
"source": [
"tstate = out['runner_state'][0]\n",
"lastobs = out['runner_state'][2]\n",
"# tstate.apply_fn(tstate.params, lastobs)\n",
"\n",
"env = Snax()\n",
"env_params = env.default_params\n",
"env = LogWrapper(env)\n",
"env = VecEnv(env)\n",
"\n",
"def _env_step(runner_state, unused):\n",
" train_state, env_state, last_obs, rng = runner_state\n",
"\n",
" # SELECT ACTION\n",
" rng, _rng = jax.random.split(rng)\n",
" pi, value = train_state.apply_fn(train_state.params, last_obs)\n",
" action = pi.sample(seed=_rng)\n",
" log_prob = pi.log_prob(action)\n",
"\n",
" # STEP ENV\n",
" rng, _rng = jax.random.split(rng)\n",
" rng_step = jax.random.split(_rng, config[\"NUM_ENVS\"])\n",
" obsv, env_state, reward, done, info = env.step(\n",
" rng_step, env_state, action, env_params\n",
" )\n",
" transition = Transition(\n",
" done, action, value, reward, log_prob, last_obs, info\n",
" )\n",
" runner_state = (train_state, env_state, obsv, rng)\n",
" return runner_state, env_state\n",
"\n",
"\n",
"\n",
"runner_state, env_logs = jax.lax.scan(\n",
" _env_step, out['runner_state'], None, 100\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TrainState(step=Array(1874688, dtype=int32, weak_type=True), apply_fn=<bound method Module.apply of ActorCritic(\n",
" # attributes\n",
" action_dim = 1\n",
" activation = 'tanh'\n",
")>, params={'params': {'Dense_0': {'bias': Array([-5.3029938e-04, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 1.9191748e-03, 0.0000000e+00, -1.7144637e-04, 2.5281330e-05,\n",
" 1.4641370e-03, 0.0000000e+00, -1.3245111e-03, 0.0000000e+00,\n",
" 0.0000000e+00, 8.3523680e-04, 0.0000000e+00, 0.0000000e+00,\n",
" 6.0847404e-05, -4.7602013e-04, 0.0000000e+00, 1.3794134e-03,\n",
" 0.0000000e+00, -7.6553306e-06, 0.0000000e+00, 2.1930558e-04,\n",
" 0.0000000e+00, -2.2188693e-03, 1.4511290e-03, 1.9425271e-03,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.1256059e-03,\n",
" -5.8398215e-04, 0.0000000e+00, -1.8540439e-03, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, -6.9769150e-05, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 6.8441045e-04, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, -8.8643702e-04, 1.3925527e-03,\n",
" -1.4111768e-04, 0.0000000e+00, 0.0000000e+00, -2.0299070e-03,\n",
" 6.8957923e-04, -1.2412324e-03, 5.5068691e-04, -5.2116567e-04,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.2125333e-04,\n",
" 9.1447402e-04, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 5.2126928e-04, 0.0000000e+00, 5.8222190e-04, 0.0000000e+00,\n",
" 0.0000000e+00, 2.2282777e-03, 1.3834524e-03, -1.0245681e-03,\n",
" 1.7563089e-04, -2.4510939e-03, 0.0000000e+00, -1.6243160e-03,\n",
" -9.4084017e-04, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" -9.0897578e-04, 0.0000000e+00, -1.0096998e-03, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.1322434e-03,\n",
" 0.0000000e+00, 0.0000000e+00, 2.2586577e-03, -2.4337969e-03,\n",
" 5.6456536e-04, 3.0193112e-03, 0.0000000e+00, 0.0000000e+00,\n",
" -2.9874803e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" -3.7863411e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, -3.2294096e-04,\n",
" 2.7185181e-04, 1.4245387e-08, -1.9866133e-03, -2.2128341e-03,\n",
" 0.0000000e+00, -1.7956822e-04, 1.4105116e-03, 0.0000000e+00,\n",
" -6.7686941e-04, 0.0000000e+00, 0.0000000e+00, 4.0784094e-04,\n",
" 0.0000000e+00, 0.0000000e+00, -1.0925150e-03, -9.4625680e-04,\n",
" -1.0735401e-03, 1.7537770e-03, -1.4924963e-03, -1.0218532e-03,\n",
" 0.0000000e+00, -3.4914708e-03, -4.7835559e-04, 1.9216866e-03,\n",
" 0.0000000e+00, 7.1237684e-04, -3.2128301e-03, -1.7769258e-03,\n",
" 2.2146765e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 6.1193650e-04, 0.0000000e+00, 0.0000000e+00, 1.7334922e-04,\n",
" -2.6329695e-03, -1.3060468e-03, 3.4790873e-03, 0.0000000e+00,\n",
" 1.2544972e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, -1.2978822e-03, -3.8260728e-04, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, -4.5437412e-04, 0.0000000e+00,\n",
" 0.0000000e+00, 7.1664042e-05, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 7.0464790e-05,\n",
" -8.9069502e-04, 0.0000000e+00, 6.6835230e-04, 0.0000000e+00,\n",
" 6.8251284e-05, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" -1.0040104e-03, -2.3794189e-07, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 2.3229128e-04, 1.5453034e-03, -5.0179427e-05,\n",
" 2.2772038e-03, -2.0145031e-03, 9.4736563e-03, 7.9449776e-05,\n",
" -7.4491683e-05, 0.0000000e+00, 2.5824676e-04, 0.0000000e+00,\n",
" 1.2736652e-03, 0.0000000e+00, 3.0285574e-04, 2.4916211e-04,\n",
" 0.0000000e+00, 0.0000000e+00, 7.7232631e-04, 5.4959516e-04,\n",
" -1.9705489e-03, 0.0000000e+00, 1.1921050e-03, -3.9399034e-04,\n",
" 0.0000000e+00, 3.7107369e-04, -4.8485940e-04, 0.0000000e+00,\n",
" 1.5376735e-04, 1.8611597e-03, -6.0213718e-04, -2.2827655e-03,\n",
" 0.0000000e+00, 0.0000000e+00, -2.4243153e-04, 0.0000000e+00,\n",
" -2.2830942e-03, 4.1451986e-04, -2.6722133e-04, 2.5362780e-04,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
" 0.0000000e+00, 0.0000000e+00, -6.0310279e-04, 0.0000000e+00,\n",
" 0.0000000e+00, -4.7796275e-04, 0.0000000e+00, 0.0000000e+00], dtype=float32), 'kernel': Array([[-0.04059862, -0.05616716, 0.05436023, ..., -0.01495443,\n",
" -0.09417699, -0.03651878],\n",
" [-0.04345348, 0.0237919 , 0.13925986, ..., -0.03692472,\n",
" 0.03002992, -0.11007421],\n",
" [ 0.09764732, -0.03158703, -0.02001419, ..., -0.14909488,\n",
" 0.02449407, 0.01721021],\n",
" ...,\n",
" [ 0.11338337, -0.04206258, 0.11930869, ..., -0.01594234,\n",
" 0.0031549 , 0.07585711],\n",
" [-0.09437636, -0.10784481, -0.13662401, ..., 0.0185915 ,\n",
" 0.04616122, -0.02840761],\n",
" [-0.03904481, -0.04892467, 0.05431201, ..., 0.1215561 ,\n",
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" [1.58691948e-09]], dtype=float32)}, 'log_std': Array([3.184018e-06], dtype=float32)}}), EmptyState())))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out['runner_state'][0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Destination /tmp/flax_ckpt/orbax/single_save already exists.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m ckpt \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrunner_state\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]}\n\u001b[1;32m 5\u001b[0m save_args \u001b[38;5;241m=\u001b[39m orbax_utils\u001b[38;5;241m.\u001b[39msave_args_from_target(ckpt)\n\u001b[0;32m----> 6\u001b[0m \u001b[43morbax_checkpointer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/tmp/flax_ckpt/orbax/single_save\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_args\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py:201\u001b[0m, in \u001b[0;36mCheckpointer.save\u001b[0;34m(self, directory, force, *args, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m directory\u001b[38;5;241m.\u001b[39mrmtree() \u001b[38;5;66;03m# Post-sync handled by create_tmp_directory.\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDestination \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdirectory\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m already exists.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 202\u001b[0m ckpt_args \u001b[38;5;241m=\u001b[39m construct_checkpoint_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handler, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 203\u001b[0m tmpdir \u001b[38;5;241m=\u001b[39m asyncio_utils\u001b[38;5;241m.\u001b[39mrun_sync(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_temporary_path(directory))\n",
"\u001b[0;31mValueError\u001b[0m: Destination /tmp/flax_ckpt/orbax/single_save already exists."
]
}
],
"source": [
"import orbax.checkpoint\n",
"from flax.training import orbax_utils\n",
"orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n",
"ckpt = {'model': out['runner_state'][0]}\n",
"save_args = orbax_utils.save_args_from_target(ckpt)\n",
"orbax_checkpointer.save('/tmp/flax_ckpt/orbax/single_save', ckpt, save_args=save_args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
2024-12-15 19:54:23 +00:00
}
],
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