restructure
This commit is contained in:
Robin Meersman
parent
b892e3777e
commit
431ecdf4b4
10 changed files with 8 additions and 27 deletions
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@ -1,536 +0,0 @@
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import datetime
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import random
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import sys
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import time
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from dataclasses import asdict, dataclass
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from functools import partial
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from typing import Any
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import flax
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import jax
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import jax.numpy as jnp
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import numpy as np
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import optax
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import tqdm
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from flax.training.train_state import TrainState
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from torch.utils.tensorboard import SummaryWriter
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from brittle_star_project.dataclasses import EpisodeStatistics, PPOArgs
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from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper
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from MLPs.mlps import (
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Actor,
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AgentParams,
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GenericDenseLayersWithActivation,
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OneDenseLayerMLP,
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Storage,
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)
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from ppo import PPO
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@jax.jit
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def linear_schedule(count, minibatch_count, update_epochs, num_iterations, learning_rate):
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frac = 1.0 - (count // (minibatch_count * update_epochs)) / num_iterations
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return learning_rate * frac
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@jax.jit
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def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray:
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return jax.vmap(lambda o: jnp.concatenate([v.flatten() for v in o.values() if v.size > 0]))(
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obs_dict
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)
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# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit
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def _get_action_and_value_noise(
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sensor: GenericDenseLayersWithActivation,
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feature_extractor: GenericDenseLayersWithActivation,
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actor: Actor,
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critic: OneDenseLayerMLP,
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agent_state: TrainState,
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next_obs: jnp.ndarray,
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key: jax.random.PRNGKey,
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):
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hidden = sensor.apply(agent_state.params["sensor_params"], next_obs)
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hidden_critic = feature_extractor.apply(
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agent_state.params["feature_extractor_params"], next_obs
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)
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# Continuous actions: sample from a Gaussian parameterized by the actor
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mean, log_std = actor.apply(agent_state.params["actor_params"], hidden)
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key, subkey = jax.random.split(key)
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noise = jax.random.normal(subkey, shape=mean.shape)
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std = jnp.exp(log_std)
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action = mean + noise * std
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logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1)
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value = critic.apply(agent_state.params["critic_params"], hidden_critic)
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return action, logprob, value.squeeze(-1), key
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# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit
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def _step_once(
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carry,
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_,
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env_step_fn,
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sensor: GenericDenseLayersWithActivation,
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feature_extractor: GenericDenseLayersWithActivation,
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actor: Actor,
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critic: OneDenseLayerMLP,
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):
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agent_state, episode_stats, obs, done, key, env_state = carry
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action, logprob, value, key = _get_action_and_value_noise(
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sensor, feature_extractor, actor, critic, agent_state, obs, key
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)
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episode_stats, env_state, (next_obs, reward, next_done) = env_step_fn(
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episode_stats, env_state, action
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)
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storage = Storage(
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obs=obs,
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actions=action,
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logprobs=logprob,
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dones=done,
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values=value,
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rewards=reward,
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returns=jnp.zeros_like(reward),
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advantages=jnp.zeros_like(reward),
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)
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return (agent_state, episode_stats, next_obs, next_done, key, env_state), storage
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# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit
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def _step_env_wrapped(episode_stats, env_state, action, env_step_fn):
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next_env_state = env_step_fn(env_state, action)
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# Extract per-environment signals from the state object
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reward = next_env_state.reward # (num_envs,)
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terminated = next_env_state.terminated # (num_envs,)
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truncated = next_env_state.truncated # (num_envs,)
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done = terminated | truncated # (num_envs,)
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new_episode_return = episode_stats.episode_returns + reward
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new_episode_length = episode_stats.episode_lengths + 1
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episode_stats = episode_stats.replace(
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episode_returns=new_episode_return * (1 - done),
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episode_lengths=new_episode_length * (1 - done),
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returned_episode_returns=jnp.where(
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done, new_episode_return, episode_stats.returned_episode_returns
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),
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returned_episode_lengths=jnp.where(
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done, new_episode_length, episode_stats.returned_episode_lengths
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),
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)
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return (
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episode_stats,
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next_env_state,
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(convert_obs_dict_to_array(next_env_state.observations), reward, done),
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)
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# jit applied in wrapper method self._rollout_jit using partial
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def _rollout_jit(
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agent_state,
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episode_stats,
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env_state,
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next_obs,
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next_done,
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key,
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max_steps,
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step_env_fn,
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sensor: GenericDenseLayersWithActivation,
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feature_extractor: GenericDenseLayersWithActivation,
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actor: Actor,
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critic: OneDenseLayerMLP,
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):
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(agent_state, episode_stats, next_obs, next_done, key, env_state), storage = jax.lax.scan(
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partial(
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_step_once,
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sensor=sensor,
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feature_extractor=feature_extractor,
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actor=actor,
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critic=critic,
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env_step_fn=step_env_fn,
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),
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(agent_state, episode_stats, next_obs, next_done, key, env_state),
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(),
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max_steps,
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)
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return agent_state, episode_stats, next_obs, next_done, storage, key, env_state
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# removed jit: used in _compute_gae_jit, so will be compiled with _compute_gae_jit
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def _compute_gae_once(carry, inp, gamma, gae_lambda):
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advantages = carry
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nextdone, nextvalues, curvalues, reward = inp
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nextnonterminal = 1.0 - nextdone
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delta = reward + gamma * nextvalues * nextnonterminal - curvalues
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advantages = delta + gamma * gae_lambda * nextnonterminal * advantages
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return advantages, advantages
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# jit applied on partial-wrapped wrapper method self._compute_gae_jit
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def _compute_gae_jit(
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agent_state, storage, next_obs, next_done, gamma, gae_lambda, num_envs, sensor, critic
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):
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next_value = critic.apply(
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agent_state.params["critic_params"],
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sensor.apply(agent_state.params["sensor_params"], next_obs),
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).squeeze(-1)
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advantages = jnp.zeros((num_envs,))
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dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0)
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values = jnp.concatenate([storage.values, next_value[None, :]], axis=0)
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_, advantages = jax.lax.scan(
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partial(_compute_gae_once, gamma=gamma, gae_lambda=gae_lambda),
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advantages,
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(dones[1:], values[1:], values[:-1], storage.rewards),
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reverse=True,
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)
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return storage.replace(advantages=advantages, returns=advantages + storage.values)
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@dataclass
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class LossInfo:
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# todo: better typing
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loss: Any
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pg_loss: Any
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v_loss: Any
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entropy_loss: Any
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approx_kl: Any
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avg_episodic_return: Any
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class PPOTrainer:
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def __init__(self, args: PPOArgs, env: BrittleStarJaxEnvWrapper, run_dir: str, run_name: str):
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self.args = args
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self.env = env
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self.run_dir = run_dir
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self.run_name = run_name
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self.writer = SummaryWriter(self.run_dir)
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self.key = jax.random.PRNGKey(args.seed)
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self.sensor, self.feature_extractor, self.actor, self.critic = self._init_agent()
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self.sensor.apply = jax.jit(self.sensor.apply)
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self.feature_extractor.apply = jax.jit(self.feature_extractor.apply)
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self.actor.apply = jax.jit(self.actor.apply)
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self.critic.apply = jax.jit(self.critic.apply)
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self._rollout_jit = jax.jit(
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partial(
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_rollout_jit,
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max_steps=self.args.num_steps,
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step_env_fn=partial(_step_env_wrapped, env_step_fn=self.env.step),
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sensor=self.sensor,
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feature_extractor=self.feature_extractor,
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actor=self.actor,
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critic=self.critic,
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)
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)
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self._compute_gae_jit = jax.jit(
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partial(
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_compute_gae_jit,
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num_envs=self.args.num_envs,
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gamma=self.args.gamma,
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gae_lambda=self.args.gae_lambda,
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sensor=self.sensor,
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critic=self.critic,
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)
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)
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self._ppo = PPO(self.args, self.sensor, self.actor, self.critic, self.feature_extractor)
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self.agent_state = self._init_agent_state()
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self.episode_stats = self._init_episode_stats()
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self._init_random()
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def _init_random(self, log: bool = True):
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if log:
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print(f"[RANDOM]: Setting random seed to {self.args.seed}")
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random.seed(self.args.seed)
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np.random.seed(self.args.seed)
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def _init_agent(self, log: bool = True):
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if log:
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print("[AGENT]: Initializing agent...")
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sensor = GenericDenseLayersWithActivation()
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feature_extractor = GenericDenseLayersWithActivation()
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actor = Actor(
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action_dim=self.env.single_action_space.shape[0]
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) # continuous actions for MJX
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critic = OneDenseLayerMLP()
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# messenger = OneDenseLayerMLP()
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return sensor, feature_extractor, actor, critic
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def _init_agent_state(self, log: bool = True) -> TrainState:
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if log:
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print("[AGENT STATE]: Initializing agent state...")
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self.key, sensor_key, actor_key, critic_key, feature_extractor_key = jax.random.split(
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self.key, 5
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)
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sample_obs = jnp.concatenate(
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[
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v.flatten()
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for v in self.env.single_observation_space.sample(
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rng=jax.random.PRNGKey(0)
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).values()
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if v.size > 0
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]
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)
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sensor_params = self.sensor.init(sensor_key, sample_obs)
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feature_extractor_params = self.feature_extractor.init(feature_extractor_key, sample_obs)
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actor_params = self.actor.init(actor_key, self.sensor.apply(sensor_params, sample_obs))
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critic_params = self.critic.init(
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critic_key, self.feature_extractor.apply(feature_extractor_params, sample_obs)
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)
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return TrainState.create(
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apply_fn=None,
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params=asdict(
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AgentParams(sensor_params, actor_params, critic_params, feature_extractor_params)
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),
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tx=optax.chain(
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optax.clip_by_global_norm(self.args.max_grad_norm),
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optax.inject_hyperparams(optax.adam)(
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learning_rate=partial(
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linear_schedule,
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minibatch_count=self.args.num_minibatches,
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update_epochs=self.args.update_epochs,
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num_iterations=self.args.num_iterations,
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learning_rate=self.args.learning_rate,
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)
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if self.args.anneal_lr
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else self.args.learning_rate,
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eps=1e-5,
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),
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),
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)
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def _init_episode_stats(self, log: bool = True) -> EpisodeStatistics:
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if log:
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print("[EPISODE STATS]: Initializing episode stats...")
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return EpisodeStatistics(
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episode_returns=jnp.zeros(self.args.num_envs, dtype=jnp.float32),
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episode_lengths=jnp.zeros(self.args.num_envs, dtype=jnp.int32),
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returned_episode_returns=jnp.zeros(self.args.num_envs, jnp.float32),
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returned_episode_lengths=jnp.zeros(self.args.num_envs, dtype=jnp.int32),
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)
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def _rollout(self, env_state, next_obs, next_done) -> tuple[Storage, ...]:
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return self._rollout_jit(
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self.agent_state,
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self.episode_stats,
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env_state,
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next_obs,
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next_done,
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self.key,
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)
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def _compute_gae(self, storage, next_obs, next_done) -> Storage:
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return self._compute_gae_jit(
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self.agent_state,
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storage,
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next_obs,
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next_done,
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)
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def _log(
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self,
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global_step,
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episode_stats,
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start_time,
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iteration_time_start,
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loss_info,
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):
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self.writer.add_scalar(
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"charts/avg_episodic_return", loss_info.avg_episodic_return, global_step
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)
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self.writer.add_scalar(
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"charts/avg_episodic_length",
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np.mean(jax.device_get(episode_stats.returned_episode_lengths)),
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global_step,
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)
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self.writer.add_scalar(
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"charts/learning_rate",
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self.agent_state.opt_state[1].hyperparams["learning_rate"].item(),
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global_step,
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)
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self.writer.add_scalar("losses/value_loss", loss_info.v_loss[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/policy_loss", loss_info.pg_loss[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/entropy", loss_info.entropy_loss[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/approx_kl", loss_info.approx_kl[-1, -1].item(), global_step)
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self.writer.add_scalar("losses/loss", loss_info.loss[-1, -1].item(), global_step)
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self.writer.add_scalar(
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"charts/SPS", int(global_step / (time.time() - start_time)), global_step
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)
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self.writer.add_scalar(
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"charts/SPS_update",
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int(self.args.num_envs * self.args.num_steps / (time.time() - iteration_time_start)),
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global_step,
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)
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def _step(
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self, env_state, next_obs, next_done, is_tty: bool, iteration: int, log: bool = True
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) -> tuple:
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if log and not is_tty and iteration == 1:
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print(f">>> [HPC] Starting first rollout (JIT): {time.ctime()}", flush=True)
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(
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self.agent_state,
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self.episode_stats,
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next_obs,
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next_done,
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storage,
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self.key,
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next_env_state,
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) = self._rollout(env_state, next_obs, next_done)
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if log and not is_tty and iteration == 1:
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print(f">>> [HPC] First rollout completed: {time.ctime()}", flush=True)
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storage = self._compute_gae(storage, next_obs, next_done)
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if log and not is_tty and iteration == 1:
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print(f">>> [HPC] Starting first PPO update (JIT): {time.ctime()}", flush=True)
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self.agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, self.key = (
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self._ppo.update_ppo(self.agent_state, storage, self.key)
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)
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if log and not is_tty and iteration == 1:
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print(f">>> [HPC] First PPO update completed: {time.ctime()}", flush=True)
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avg_episodic_return = float(
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jnp.mean(jax.device_get(self.episode_stats.returned_episode_returns)).item()
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)
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return (
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next_env_state,
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next_obs,
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next_done,
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LossInfo(
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loss=loss,
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pg_loss=pg_loss,
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v_loss=v_loss,
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entropy_loss=entropy_loss,
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approx_kl=approx_kl,
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avg_episodic_return=avg_episodic_return,
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),
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)
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def _close(self):
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self.env.close()
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self.writer.close()
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def _save_model(self, model_path: str, log: bool = True):
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if log:
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print(f"[SAVE]: Saving the model to: {model_path}...")
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with open(model_path, "wb") as f:
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f.write(
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flax.serialization.to_bytes(
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[
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vars(self.args),
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[
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self.agent_state.params["sensor_params"],
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self.agent_state.params["actor_params"],
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self.agent_state.params["critic_params"],
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self.agent_state.params["feature_extractor_params"],
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],
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]
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)
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)
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def train(self, log: bool = True):
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"""
|
||||
Train the PPO agent for a specified number of iterations
|
||||
(passed through PPOArgs in constructor).
|
||||
Closes the environment at the end of training.
|
||||
"""
|
||||
if log:
|
||||
print(f"running name: {self.run_name}")
|
||||
|
||||
is_tty = sys.stdout.isatty()
|
||||
if log:
|
||||
print("[TRAIN]: Resetting environment...")
|
||||
|
||||
if not is_tty:
|
||||
print(f">>> [HPC] Initial reset started: {time.ctime()}", flush=True)
|
||||
|
||||
env_state = self.env.reset(seed=self.args.seed)
|
||||
next_obs = convert_obs_dict_to_array(env_state.observations)
|
||||
next_done = jnp.zeros(self.args.num_envs, dtype=jnp.bool_)
|
||||
|
||||
if log and not is_tty:
|
||||
print(f">>> [HPC] Initial reset completed: {time.ctime()}", flush=True)
|
||||
|
||||
global_step = 0
|
||||
start_time = time.time()
|
||||
|
||||
if self.args.track:
|
||||
import wandb
|
||||
|
||||
if log:
|
||||
print("[TRAIN]: Initializing Weights and Biases...")
|
||||
|
||||
wandb.init(
|
||||
project=self.args.wandb_project_name,
|
||||
entity=self.args.wandb_entity,
|
||||
sync_tensorboard=True,
|
||||
config=vars(self.args),
|
||||
name=self.run_name,
|
||||
save_code=True,
|
||||
)
|
||||
|
||||
if log:
|
||||
print("[TRAIN]: Adding hyperparameters to TensorBoard...")
|
||||
|
||||
self.writer.add_text(
|
||||
"hyperparameters",
|
||||
"|param|value|\n|---|---|\n"
|
||||
+ "\n".join(f"|{k}|{v}|" for k, v in vars(self.args).items()),
|
||||
)
|
||||
|
||||
iter_bar = tqdm.tqdm(
|
||||
range(1, self.args.num_iterations + 1),
|
||||
disable=not is_tty,
|
||||
)
|
||||
for iteration in iter_bar:
|
||||
iteration_time_start = time.time()
|
||||
|
||||
env_state, next_obs, next_done, loss_info = self._step(
|
||||
env_state, next_obs, next_done, is_tty=is_tty, iteration=iteration
|
||||
)
|
||||
|
||||
global_step += self.args.num_steps * self.args.num_envs
|
||||
self._log(global_step, self.episode_stats, start_time, iteration_time_start, loss_info)
|
||||
|
||||
if log and not is_tty:
|
||||
sps = int(global_step / (time.time() - start_time))
|
||||
remaining_steps = self.args.total_timesteps - global_step
|
||||
eta_seconds = int(remaining_steps / sps) if sps > 0 else 0
|
||||
eta_str = str(datetime.timedelta(seconds=eta_seconds))
|
||||
|
||||
print(
|
||||
f"Iteration {iteration}/{self.args.num_iterations} | "
|
||||
f"Step {global_step}/{self.args.total_timesteps} | "
|
||||
f"SPS {sps} | "
|
||||
f"Return {loss_info.avg_episodic_return:.4f} | "
|
||||
f"ETA {eta_str}",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
if self.args.save_model:
|
||||
model_path = f"{self.run_dir}/{self.args.exp_name}.cleanrl_model"
|
||||
self._save_model(model_path=model_path)
|
||||
|
||||
self._close()
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
from .plot import simple_plot
|
||||
|
||||
__all__ = ["simple_plot"]
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
def simple_plot(x: list, y: list, show_window: bool = False, filename: str = "plot.png") -> None:
|
||||
plt.plot(x, y)
|
||||
plt.savefig(filename)
|
||||
|
||||
if show_window:
|
||||
# blocks until window is closed
|
||||
plt.show()
|
||||
|
||||
plt.close()
|
||||
|
|
@ -1,95 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
from brittle_star_project import (
|
||||
Backend,
|
||||
BrittleStarEnv,
|
||||
BrittleStarEnvFactory,
|
||||
SimulationConfig,
|
||||
simulate_policy,
|
||||
)
|
||||
from brittle_star_project.environment import from_json
|
||||
from brittle_star_project.rl import RLModel # imports concrete models via rl.__init__
|
||||
from brittle_star_project.rl.base import get_rl_model_registry
|
||||
|
||||
MODEL_BY_NAME = get_rl_model_registry()
|
||||
MODEL_OPTIONS = sorted(MODEL_BY_NAME)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
p = argparse.ArgumentParser(description="Simulate a trained policy in the MuJoCo viewer.")
|
||||
p.add_argument(
|
||||
"--model",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Path to a saved model artifact. If omitted, a model is created from --model-type.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--model-type",
|
||||
choices=MODEL_OPTIONS,
|
||||
default="random",
|
||||
help="Which model class to instantiate when --model is omitted.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--backend",
|
||||
choices=[b for b in Backend],
|
||||
default=Backend.MJX,
|
||||
)
|
||||
p.add_argument("--seed", type=int, default=None)
|
||||
return p.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
|
||||
morphology_cfg, arena_cfg, env_cfg = from_json("../configs/test.json")
|
||||
|
||||
# ======= ENVIRONMENT SETUP =======
|
||||
|
||||
backend = args.backend
|
||||
|
||||
factory = BrittleStarEnvFactory()
|
||||
raw_env = factory.create_environment(backend, morphology_cfg, arena_cfg, env_cfg)
|
||||
env = BrittleStarEnv(raw_env, backend=backend, config=env_cfg)
|
||||
|
||||
seed_for_env = int(args.seed) if args.seed is not None else 0
|
||||
state = env.reset(seed=seed_for_env)
|
||||
|
||||
# ======= MODEL SETUP =======
|
||||
|
||||
# Extract the number of actuators (nu) from the environment's model, so we can pass it to the
|
||||
# policy/model.
|
||||
nu = int(state.mj_model.nu)
|
||||
|
||||
if args.model is not None:
|
||||
model_path = Path(args.model)
|
||||
policy = RLModel.load(model_path)
|
||||
if hasattr(policy, "nu"):
|
||||
policy.nu = nu
|
||||
else:
|
||||
model_cls = MODEL_BY_NAME[str(args.model_type)]
|
||||
policy = model_cls(seed=seed_for_env)
|
||||
if hasattr(policy, "nu"):
|
||||
policy.nu = nu
|
||||
|
||||
# If the policy/model has a `seed` attribute, use the provided seed (or default) to reset it.
|
||||
default_seed = int(getattr(policy, "seed", seed_for_env))
|
||||
if args.seed is not None and hasattr(policy, "reset"):
|
||||
policy.reset(int(args.seed))
|
||||
|
||||
# ======= SIMULATION =======
|
||||
|
||||
rollout_cfg = SimulationConfig(
|
||||
realtime=True,
|
||||
seed=int(args.seed) if args.seed is not None else default_seed,
|
||||
)
|
||||
|
||||
simulate_policy(policy, rollout_cfg, state)
|
||||
|
||||
env.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
import subprocess
|
||||
import time
|
||||
|
||||
import torch
|
||||
import tyro
|
||||
import yaml
|
||||
import os
|
||||
|
||||
from brittle_star_project.dataclasses import PPOArgs
|
||||
from PPOTrainer import PPOTrainer
|
||||
from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper
|
||||
|
||||
|
||||
def make_env(config_path: str | None, num_envs: int) -> BrittleStarJaxEnvWrapper:
|
||||
if config_path is None:
|
||||
return BrittleStarJaxEnvWrapper.default(num_envs=num_envs)
|
||||
return BrittleStarJaxEnvWrapper.from_config(config_path, num_envs=num_envs)
|
||||
|
||||
|
||||
def parse_args(log: bool = True) -> PPOArgs:
|
||||
temp_args = tyro.cli(PPOArgs)
|
||||
|
||||
if temp_args.hyperparameter_config_path is not None:
|
||||
if log:
|
||||
print(f"Loading hyperparameter config from {temp_args.hyperparameter_config_path}")
|
||||
|
||||
with open(temp_args.hyperparameter_config_path, "r") as f:
|
||||
config = yaml.safe_load(f)
|
||||
if config:
|
||||
# parse PPOArgs with defaults from yaml.
|
||||
for key, value in config.items():
|
||||
if hasattr(temp_args, key):
|
||||
setattr(temp_args, key, value)
|
||||
|
||||
# Reparse CLI to ensure they OVERRIDE the yaml
|
||||
args = tyro.cli(PPOArgs, default=temp_args)
|
||||
else:
|
||||
if log:
|
||||
print("No hyperparameter config provided, using default config")
|
||||
|
||||
args = temp_args
|
||||
return args
|
||||
|
||||
|
||||
def get_git_hash() -> str:
|
||||
try:
|
||||
return (
|
||||
subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode("ascii").strip()
|
||||
)
|
||||
except subprocess.CalledProcessError | UnicodeDecodeError:
|
||||
return "none"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parse_args()
|
||||
|
||||
args.batch_size = args.num_envs * args.num_steps
|
||||
args.minibatch_size = args.batch_size // args.num_minibatches
|
||||
args.num_iterations = args.total_timesteps // args.batch_size
|
||||
|
||||
git_hash = get_git_hash()
|
||||
run_name = f"{args.exp_name}__seed_{args.seed}__{git_hash}__{int(time.time())}"
|
||||
if args.run_dir is None:
|
||||
run_dir = f"runs/{run_name}"
|
||||
else:
|
||||
run_dir = args.run_dir
|
||||
|
||||
os.makedirs(run_dir, exist_ok=True)
|
||||
|
||||
env = make_env(args.env_config_path, args.num_envs)
|
||||
|
||||
torch.backends.cudnn.deterministic = args.torch_deterministic
|
||||
|
||||
ppo_trainer = PPOTrainer(args, env, run_dir, run_name)
|
||||
ppo_trainer.train()
|
||||
Reference in a new issue