100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
import torch
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from torchrl.modules import ProbabilisticActor
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from torchrl.objectives.value.advantages import GAE
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from fancy_rl.algos.on_policy import OnPolicy
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from fancy_rl.policy import Actor, Critic
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from fancy_rl.objectives import TRPLLoss
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class TRPL(OnPolicy):
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def __init__(
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self,
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env_spec,
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loggers=None,
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actor_hidden_sizes=[64, 64],
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critic_hidden_sizes=[64, 64],
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actor_activation_fn="Tanh",
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critic_activation_fn="Tanh",
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proj_layer_type=None,
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learning_rate=3e-4,
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n_steps=2048,
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batch_size=64,
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n_epochs=10,
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gamma=0.99,
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gae_lambda=0.95,
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total_timesteps=1e6,
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eval_interval=2048,
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eval_deterministic=True,
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entropy_coef=0.01,
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critic_coef=0.5,
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trust_region_coef=10.0,
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normalize_advantage=False,
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device=None,
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env_spec_eval=None,
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eval_episodes=10,
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):
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device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.device = device
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self.trust_region_layer = None # TODO: from proj_layer_type
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self.trust_region_coef = trust_region_coef
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# Initialize environment to get observation and action space sizes
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self.env_spec = env_spec
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env = self.make_env()
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obs_space = env.observation_space
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act_space = env.action_space
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self.critic = Critic(obs_space, critic_hidden_sizes, critic_activation_fn, device)
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actor_net = Actor(obs_space, act_space, actor_hidden_sizes, actor_activation_fn, device)
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raw_actor = ProbabilisticActor(
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module=actor_net,
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in_keys=["loc", "scale"],
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out_keys=["action"],
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distribution_class=torch.distributions.Normal,
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return_log_prob=True
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)
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self.actor = raw_actor # TODO: Proj here
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optimizers = {
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"actor": torch.optim.Adam(self.actor.parameters(), lr=learning_rate),
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"critic": torch.optim.Adam(self.critic.parameters(), lr=learning_rate)
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}
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super().__init__(
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env_spec=env_spec,
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loggers=loggers,
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optimizers=optimizers,
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learning_rate=learning_rate,
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n_steps=n_steps,
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batch_size=batch_size,
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n_epochs=n_epochs,
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gamma=gamma,
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total_timesteps=total_timesteps,
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eval_interval=eval_interval,
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eval_deterministic=eval_deterministic,
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entropy_coef=entropy_coef,
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critic_coef=critic_coef,
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normalize_advantage=normalize_advantage,
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device=device,
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env_spec_eval=env_spec_eval,
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eval_episodes=eval_episodes,
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)
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self.adv_module = GAE(
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gamma=self.gamma,
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lmbda=gae_lambda,
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value_network=self.critic,
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average_gae=False,
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)
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self.loss_module = TRPLLoss(
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actor_network=self.actor,
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critic_network=self.critic,
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trust_region_layer=self.trust_region_layer,
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loss_critic_type='l2',
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entropy_coef=self.entropy_coef,
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critic_coef=self.critic_coef,
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trust_region_coef=self.trust_region_coef,
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normalize_advantage=self.normalize_advantage,
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)
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