import torch as th import torch.nn as nn from torch.distributions import Normal from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork class Actor(BaseNetwork): def __init__(self, obs_dim, action_dim, net_arch, activation_fn=nn.ReLU, use_sde=False, log_std_init=-2, clip_noise=None, lr_sde=3e-4, full_std=False): super(Actor, self).__init__() self.latent_pi, self.log_std = None, None self.weights_dist, self.exploration_mat = None, None self.use_sde, self.sde_optimizer = use_sde, None self.action_dim = action_dim self.full_std = full_std if use_sde: latent_dim = net_arch[-1] latent_pi = create_mlp(obs_dim, -1, net_arch, activation_fn, squash_out=False) self.latent_pi = nn.Sequential(*latent_pi) if full_std: self.log_std = nn.Parameter(th.ones(latent_dim, action_dim) * log_std_init, requires_grad=True) else: # Reduce the number of parameters: self.log_std = nn.Parameter(th.ones(latent_dim, 1) * log_std_init, requires_grad=True) self.latent_dim = latent_dim self.actor_net = nn.Sequential(nn.Linear(net_arch[-1], action_dim), nn.Tanh()) self.clip_noise = clip_noise self.sde_optimizer = th.optim.Adam([self.log_std], lr=lr_sde) self.reset_noise() else: actor_net = create_mlp(obs_dim, action_dim, net_arch, activation_fn, squash_out=True) self.actor_net = nn.Sequential(*actor_net) def get_log_std(self): if self.full_std: return self.log_std # Reduce the number of parameters: return th.ones((self.latent_dim, self.action_dim)).to(self.log_std.device) * self.log_std def evaluate_actions(self, obs, action): with th.no_grad(): latent_pi = self.latent_pi(obs) mean_actions = self.actor_net(latent_pi) variance = th.mm(latent_pi ** 2, th.exp(self.get_log_std()) ** 2) distribution = Normal(mean_actions, th.sqrt(variance + 1e-5)) log_prob = distribution.log_prob(action) if len(log_prob.shape) > 1: log_prob = log_prob.sum(axis=1) else: log_prob = log_prob.sum() return log_prob, distribution.entropy() def reset_noise(self): self.weights_dist = Normal(th.zeros_like(self.get_log_std()), th.exp(self.get_log_std())) self.exploration_mat = self.weights_dist.rsample() def forward(self, obs, deterministic=True): if self.use_sde: latent_pi = self.latent_pi(obs) if deterministic: return self.actor_net(latent_pi) noise = th.mm(latent_pi.detach(), self.exploration_mat) if self.clip_noise is not None: noise = th.clamp(noise, -self.clip_noise, self.clip_noise) # TODO: Replace with squashing -> need to account for that in the sde update # return th.clamp(self.actor_net(latent_pi) + noise, -1, 1) # NOTE: the clipping is done in the rollout for now return self.actor_net(latent_pi) + noise else: return self.actor_net(obs) class Critic(BaseNetwork): def __init__(self, obs_dim, action_dim, net_arch, activation_fn=nn.ReLU): super(Critic, self).__init__() q1_net = create_mlp(obs_dim + action_dim, 1, net_arch, activation_fn) self.q1_net = nn.Sequential(*q1_net) q2_net = create_mlp(obs_dim + action_dim, 1, net_arch, activation_fn) self.q2_net = nn.Sequential(*q2_net) self.q_networks = [self.q1_net, self.q2_net] def forward(self, obs, action): qvalue_input = th.cat([obs, action], dim=1) return [q_net(qvalue_input) for q_net in self.q_networks] def q1_forward(self, obs, action): return self.q_networks[0](th.cat([obs, action], dim=1)) class TD3Policy(BasePolicy): def __init__(self, observation_space, action_space, learning_rate, net_arch=None, device='cpu', activation_fn=nn.ReLU, use_sde=False, log_std_init=-2, clip_noise=None, lr_sde=3e-4): super(TD3Policy, self).__init__(observation_space, action_space, device) if net_arch is None: net_arch = [400, 300] self.obs_dim = self.observation_space.shape[0] self.action_dim = self.action_space.shape[0] self.net_arch = net_arch self.activation_fn = activation_fn self.net_args = { 'obs_dim': self.obs_dim, 'action_dim': self.action_dim, 'net_arch': self.net_arch, 'activation_fn': self.activation_fn } self.actor_kwargs = self.net_args.copy() self.actor_kwargs['use_sde'] = use_sde self.actor_kwargs['log_std_init'] = log_std_init self.actor_kwargs['clip_noise'] = clip_noise self.actor_kwargs['lr_sde'] = lr_sde self.actor, self.actor_target = None, None self.critic, self.critic_target = None, None self.use_sde = use_sde self.log_std_init = log_std_init self._build(learning_rate) def _build(self, learning_rate): self.actor = self.make_actor() self.actor_target = self.make_actor() self.actor_target.load_state_dict(self.actor.state_dict()) self.actor.optimizer = th.optim.Adam(self.actor.parameters(), lr=learning_rate(1)) self.critic = self.make_critic() self.critic_target = self.make_critic() self.critic_target.load_state_dict(self.critic.state_dict()) self.critic.optimizer = th.optim.Adam(self.critic.parameters(), lr=learning_rate(1)) def reset_noise(self): return self.actor.reset_noise() def make_actor(self): return Actor(**self.actor_kwargs).to(self.device) def make_critic(self): return Critic(**self.net_args).to(self.device) def forward(self, obs, deterministic=True): return self.actor(obs, deterministic=deterministic) MlpPolicy = TD3Policy register_policy("MlpPolicy", MlpPolicy)