import torch as th import torch.nn as nn from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork, \ create_sde_feature_extractor from torchy_baselines.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution # CAP the standard deviation of the actor LOG_STD_MAX = 2 LOG_STD_MIN = -20 class LeakyClip(nn.Module): """ Cip values outside a certain range (it is not a hard clip, there is a small slope to have non-zero gradient) :param min_val: (float) :param max_val: (float) :param slope: (float) """ def __init__(self, min_val=-2.0, max_val=2.0, slope=0.01): super(LeakyClip, self).__init__() self.min_val = min_val self.max_val = max_val self.slope = slope def forward(self, x): linear_part = x * (x >= self.min_val) * (x <= self.max_val) above_max_val = self.slope * (x - self.max_val) * (x > self.max_val) below_min_val = self.slope * (x - self.min_val) * (x < self.min_val) return linear_part + below_min_val + above_max_val class Actor(BaseNetwork): """ Actor network (policy) for SAC. :param obs_dim: (int) Dimension of the observation :param action_dim: (int) Dimension of the action space :param net_arch: ([int]) Network architecture :param activation_fn: (nn.Module) Activation function :param use_sde: (bool) Whether to use State Dependent Exploration or not :param log_std_init: (float) Initial value for the log standard deviation :param full_std: (bool) Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using SDE. :param sde_net_arch: ([int]) Network architecture for extracting features when using SDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: (bool) Use `expln()` function instead of `exp()` when using SDE to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, `exp()` is usually enough. """ def __init__(self, obs_dim, action_dim, net_arch, activation_fn=nn.ReLU, use_sde=False, log_std_init=-3, full_std=True, sde_net_arch=None, use_expln=False): super(Actor, self).__init__() latent_pi_net = create_mlp(obs_dim, -1, net_arch, activation_fn) self.latent_pi = nn.Sequential(*latent_pi_net) self.use_sde = use_sde self.sde_feature_extractor = None if self.use_sde: latent_sde_dim = net_arch[-1] # Separate feature extractor for SDE if sde_net_arch is not None: self.sde_feature_extractor, latent_sde_dim = create_sde_feature_extractor(obs_dim, sde_net_arch, activation_fn) # TODO: check for the learn_features self.action_dist = StateDependentNoiseDistribution(action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True) self.mu, self.log_std = self.action_dist.proba_distribution_net(latent_dim=net_arch[-1], latent_sde_dim=latent_sde_dim, log_std_init=log_std_init) # Avoid saturation by limiting the mean of the Gaussian to be in [-1, 1] # self.mu = nn.Sequential(self.mu, nn.Tanh()) self.mu = nn.Sequential(self.mu, nn.Hardtanh(min_val=-2.0, max_val=2.0)) # Small positive slope to have non-zero gradient # self.mu = nn.Sequential(self.mu, LeakyClip()) else: self.action_dist = SquashedDiagGaussianDistribution(action_dim) self.mu = nn.Linear(net_arch[-1], action_dim) self.log_std = nn.Linear(net_arch[-1], action_dim) def get_std(self): """ Retrieve the standard deviation of the action distribution. Only useful when using SDE. It corresponds to `th.exp(log_std)` in the normal case, but is slightly different when using `expln` function (cf StateDependentNoiseDistribution doc). :return: (th.Tensor) """ assert isinstance(self.action_dist, StateDependentNoiseDistribution), 'get_std() is only available when using SDE' return self.action_dist.get_std(self.log_std) def reset_noise(self, batch_size=1): """ Sample new weights for the exploration matrix, when using SDE. :param batch_size: (int) """ assert isinstance(self.action_dist, StateDependentNoiseDistribution), 'reset_noise() is only available when using SDE' self.action_dist.sample_weights(self.log_std, batch_size=batch_size) def _get_latent(self, obs): latent_pi = self.latent_pi(obs) if self.sde_feature_extractor is not None: latent_sde = self.sde_feature_extractor(obs) else: latent_sde = latent_pi return latent_pi, latent_sde def get_action_dist_params(self, obs): latent_pi, latent_sde = self._get_latent(obs) if self.use_sde: mean_actions, log_std = self.mu(latent_pi), self.log_std else: mean_actions, log_std = self.mu(latent_pi), self.log_std(latent_pi) # Original Implementation to cap the standard deviation log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX) return mean_actions, log_std, latent_sde def forward(self, obs, deterministic=False): mean_actions, log_std, latent_sde = self.get_action_dist_params(obs) if self.use_sde: # Note the action is squashed action, _ = self.action_dist.proba_distribution(mean_actions, log_std, latent_sde, deterministic=deterministic) else: # Note the action is squashed action, _ = self.action_dist.proba_distribution(mean_actions, log_std, deterministic=deterministic) return action def action_log_prob(self, obs): mean_actions, log_std, latent_sde = self.get_action_dist_params(obs) if self.use_sde: action, log_prob = self.action_dist.log_prob_from_params(mean_actions, self.log_std, latent_sde) else: action, log_prob = self.action_dist.log_prob_from_params(mean_actions, log_std) return action, log_prob class Critic(BaseNetwork): """ Critic network (q-value function) for SAC. :param obs_dim: (int) Dimension of the observation :param action_dim: (int) Dimension of the action space :param net_arch: ([int]) Network architecture :param activation_fn: (nn.Module) Activation function """ 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 SACPolicy(BasePolicy): """ Policy class (with both actor and critic) for SAC. :param observation_space: (gym.spaces.Space) Observation space :param action_space: (gym.spaces.Space) Action space :param learning_rate: (callable) Learning rate schedule (could be constant) :param net_arch: ([int or dict]) The specification of the policy and value networks. :param device: (str or th.device) Device on which the code should run. :param activation_fn: (nn.Module) Activation function :param use_sde: (bool) Whether to use State Dependent Exploration or not :param log_std_init: (float) Initial value for the log standard deviation :param sde_net_arch: ([int]) Network architecture for extracting features when using SDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: (bool) Use `expln()` function instead of `exp()` when using SDE to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, `exp()` is usually enough. """ def __init__(self, observation_space, action_space, learning_rate, net_arch=None, device='cpu', activation_fn=nn.ReLU, use_sde=False, log_std_init=-3, sde_net_arch=None, use_expln=False): super(SACPolicy, self).__init__(observation_space, action_space, device) if net_arch is None: net_arch = [256, 256] 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() sde_kwargs = { 'use_sde': use_sde, 'log_std_init': log_std_init, 'sde_net_arch': sde_net_arch, 'use_expln': use_expln } self.actor_kwargs.update(sde_kwargs) self.actor, self.actor_target = None, None self.critic, self.critic_target = None, None self._build(learning_rate) def _build(self, learning_rate): self.actor = self.make_actor() 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 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): return self.actor(obs) MlpPolicy = SACPolicy register_policy("MlpPolicy", MlpPolicy)