resolving conflicts

# Conflicts:
#	torchy_baselines/a2c/a2c.py
#	torchy_baselines/ppo/ppo.py

Added optimizer params test

setup.py

@@ -34,7 +34,7 @@ setup(name='torchy_baselines',
       license="MIT",
       long_description="",
       long_description_content_type='text/markdown',
-      version="0.0.4",
+      version="0.0.5a",
       )
 
 # python setup.py sdist

tests/test_distributions.py

@@ -0,0 +1,20 @@
+import numpy as np
+import torch as th
+
+from torchy_baselines.common.distributions import DiagGaussianDistribution, SquashedDiagGaussianDistribution,\
+    CategoricalDistribution, TanhBijector
+
+# TODO: more tests for the other distributions
+def test_bijector():
+    """
+    Test TanhBijector
+    """
+    actions = th.ones(5) * 2.0
+
+    bijector = TanhBijector()
+
+    squashed_actions = bijector.forward(actions)
+    # Check that the boundaries are not violated
+    assert th.max(th.abs(squashed_actions)) <= 1.0
+    # Check the inverse method
+    assert th.isclose(TanhBijector.inverse(squashed_actions), actions).all()

tests/test_sde.py

@@ -0,0 +1,60 @@
+import pytest
+
+import gym
+import torch as th
+from torch.distributions import Normal
+
+from torchy_baselines import A2C
+from torchy_baselines.common.vec_env import DummyVecEnv, VecNormalize
+from torchy_baselines.common.monitor import Monitor
+
+
+def test_state_dependent_exploration():
+    """
+    Check that the gradient correspond to the expected one
+    """
+    n_states = 2
+    state_dim = 3
+    # TODO: fix for action_dim > 1
+    action_dim = 1
+    sigma = th.ones(state_dim, 1, requires_grad=True)
+    # Reduce the number of parameters
+    # sigma_ = th.ones(state_dim, action_dim) * sigma_
+
+    # weights_dist = Normal(th.zeros_like(log_sigma), th.exp(log_sigma))
+    th.manual_seed(2)
+    weights_dist = Normal(th.zeros_like(sigma), sigma)
+
+    weights = weights_dist.rsample()
+    state = th.rand(n_states, state_dim)
+    mu = th.ones(action_dim)
+    # print(weights.shape, state.shape)
+    noise = th.mm(state, weights)
+
+    variance = th.mm(state ** 2, sigma ** 2)
+    action_dist = Normal(mu, th.sqrt(variance))
+
+    loss = action_dist.log_prob((mu + noise).detach()).mean()
+    loss.backward()
+
+    # From Rueckstiess paper
+    grad = th.zeros_like(sigma)
+    for j in range(action_dim):
+        for i in range(state_dim):
+            a = ((noise[:, j] ** 2 - variance[:, j]) / (variance[:, j] ** 2)) * (state[:, i] ** 2 * sigma[i, j])
+            grad[i, j] = a.mean()
+
+    # sigma.grad should be equal to grad
+    assert sigma.grad.allclose(grad)
+
+
+@pytest.mark.parametrize("model_class", [A2C])
+def test_state_dependent_noise(model_class):
+    env_id = 'MountainCarContinuous-v0'
+
+    env = VecNormalize(DummyVecEnv([lambda: Monitor(gym.make(env_id))]), norm_reward=True)
+    eval_env = VecNormalize(DummyVecEnv([lambda: Monitor(gym.make(env_id))]), training=False, norm_reward=False)
+
+    model = model_class('MlpPolicy', env, n_steps=200, use_sde=True, ent_coef=0.00, verbose=1, learning_rate=3e-4,
+                        policy_kwargs=dict(log_std_init=0.0, ortho_init=False), seed=None)
+    model.learn(total_timesteps=int(1000), log_interval=5, eval_freq=500, eval_env=eval_env)

torchy_baselines/a2c/a2c.py

@@ -5,6 +5,7 @@ import torch.nn.functional as F
 from torchy_baselines.common.utils import explained_variance
 from torchy_baselines.ppo.ppo import PPO
 from torchy_baselines.ppo.policies import PPOPolicy
+from torchy_baselines.common import logger
 
 
 class A2C(PPO):
@@ -30,6 +31,8 @@ class A2C(PPO):
     :param rms_prop_eps: (float) RMSProp epsilon. It stabilizes square root computation in denominator
         of RMSProp update
     :param use_rms_prop: (bool) Whether to use RMSprop (default) or Adam as optimizer
+    :param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
+        instead of action noise exploration (default: False)
     :param normalize_advantage: (bool) Whether to normalize or not the advantage
     :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
     :param create_eval_env: (bool) Whether to create a second environment that will be
@@ -45,7 +48,7 @@ class A2C(PPO):
     def __init__(self, policy, env, learning_rate=7e-4,
                  n_steps=5, gamma=0.99, gae_lambda=1.0,
                  ent_coef=0.0, vf_coef=0.5, max_grad_norm=0.5,
-                 rms_prop_eps=1e-5, use_rms_prop=True,
+                 rms_prop_eps=1e-5, use_rms_prop=True, use_sde=False,
                  normalize_advantage=False, tensorboard_log=None, create_eval_env=False,
                  policy_kwargs=None, verbose=0, seed=0, device='auto',
                  _init_setup_model=True):
@@ -53,7 +56,7 @@ class A2C(PPO):
         super(A2C, self).__init__(policy, env, learning_rate=learning_rate,
                                   n_steps=n_steps, batch_size=None, n_epochs=1,
                                   gamma=gamma, gae_lambda=gae_lambda, ent_coef=ent_coef,
-                                  vf_coef=vf_coef, max_grad_norm=max_grad_norm,
+                                  vf_coef=vf_coef, max_grad_norm=max_grad_norm, use_sde=use_sde,
                                   tensorboard_log=tensorboard_log, policy_kwargs=policy_kwargs,
                                   verbose=verbose, device=device, create_eval_env=create_eval_env,
                                   seed=seed, _init_setup_model=False)
@@ -73,7 +76,6 @@ class A2C(PPO):
                                                      eps=self.rms_prop_eps, weight_decay=0)
 
     def train(self, gradient_steps, batch_size=None):
-
         # Update optimizer learning rate
         self._update_learning_rate(self.policy.optimizer)
         # A2C with gradient_steps > 1 does not make sense
@@ -113,10 +115,16 @@ class A2C(PPO):
             # Clip grad norm
             th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
             self.policy.optimizer.step()
-            # approx_kl_divs.append(th.mean(old_log_prob - log_prob).detach().cpu().numpy())
 
-            # print(explained_variance(self.rollout_buffer.returns.flatten().cpu().numpy(),
-            #                          self.rollout_buffer.values.flatten().cpu().numpy()))
+        explained_var = explained_variance(self.rollout_buffer.returns.flatten().cpu().numpy(),
+                                           self.rollout_buffer.values.flatten().cpu().numpy())
+
+        logger.logkv("explained_variance", explained_var)
+        logger.logkv("entropy", entropy.mean().item())
+        logger.logkv("policy_loss", policy_loss.item())
+        logger.logkv("value_loss", value_loss.item())
+        if hasattr(self.policy, 'log_std'):
+            logger.logkv("std", th.exp(self.policy.log_std).mean().item())
 
     def learn(self, total_timesteps, callback=None, log_interval=100,
               eval_env=None, eval_freq=-1, n_eval_episodes=5, tb_log_name="A2C", reset_num_timesteps=True):

torchy_baselines/common/base_class.py

@@ -355,7 +355,15 @@ class BaseRLModel(object):
 
         return data, params, opt_params
 
-    def set_random_seed(self, seed=0):
+    def set_random_seed(self, seed=None):
+        """
+        Set the seed of the pseudo-random generators
+        (python, numpy, pytorch, gym, action_space)
+
+        :param seed: (int)
+        """
+        if seed is None:
+            return
         set_random_seed(seed, using_cuda=self.device == th.device('cuda'))
         self.action_space.seed(seed)
         if self.env is not None:

torchy_baselines/common/buffers.py

@@ -113,22 +113,42 @@ class RolloutBuffer(BaseBuffer):
         self.generator_ready = False
         super(RolloutBuffer, self).reset()
 
-    def compute_returns_and_advantage(self, last_value, dones=False):
+    def compute_returns_and_advantage(self, last_value, dones=False, use_gae=True):
         """
-        From PPO2
+        From Stable-Baselines PPO2
+        :param last_value: (th.Tensor)
+        :param dones: ([bool])
+        :param use_gae: (bool) Whether to use Generalized Advantage Estimation
+            or normal advantage for advantage computation.
         """
-        last_gae_lam = 0
-        for step in reversed(range(self.buffer_size)):
-            if step == self.buffer_size - 1:
-                next_non_terminal = th.FloatTensor(1.0 - dones)
-                next_value = last_value.clone().cpu().flatten()
-            else:
-                next_non_terminal = 1.0 - self.dones[step + 1]
-                next_value = self.values[step + 1]
-            delta = self.rewards[step] + self.gamma * next_value * next_non_terminal - self.values[step]
-            last_gae_lam = delta + self.gamma * self.gae_lambda * next_non_terminal * last_gae_lam
-            self.advantages[step] = last_gae_lam
-        self.returns = self.advantages + self.values
+        if use_gae:
+            last_gae_lam = 0
+            for step in reversed(range(self.buffer_size)):
+                if step == self.buffer_size - 1:
+                    next_non_terminal = th.FloatTensor(1.0 - dones)
+                    next_value = last_value.clone().cpu().flatten()
+                else:
+                    next_non_terminal = 1.0 - self.dones[step + 1]
+                    next_value = self.values[step + 1]
+                delta = self.rewards[step] + self.gamma * next_value * next_non_terminal - self.values[step]
+                last_gae_lam = delta + self.gamma * self.gae_lambda * next_non_terminal * last_gae_lam
+                self.advantages[step] = last_gae_lam
+            self.returns = self.advantages + self.values
+        else:
+            # Discounted return with value bootstrap
+            # Note: this is equivalent to GAE computation
+            # with gae_lambda = 1.0
+            last_return = 0.0
+            for step in reversed(range(self.buffer_size)):
+                if step == self.buffer_size - 1:
+                    next_non_terminal = th.FloatTensor(1.0 - dones)
+                    next_value = last_value.clone().cpu().flatten()
+                    last_return = self.rewards[step] + next_non_terminal * next_value
+                else:
+                    next_non_terminal = 1.0 - self.dones[step + 1]
+                    last_return = self.rewards[step] + self.gamma * last_return * next_non_terminal
+                self.returns[step] = last_return
+            self.advantages = self.returns - self.values
 
     def add(self, obs, action, reward, done, value, log_prob):
         if len(log_prob.shape) == 0:

torchy_baselines/common/distributions.py

@@ -2,6 +2,7 @@ import numpy as np
 import torch as th
 import torch.nn as nn
 from torch.distributions import Normal, Categorical
+import torch.nn.functional as F
 from gym import spaces
 
 
@@ -45,6 +46,12 @@ class Distribution(object):
 
 
 class DiagGaussianDistribution(Distribution):
+    """
+    Gaussian distribution with diagonal covariance matrix,
+    for continuous actions.
+
+    :param action_dim: (int)  Number of continuous actions
+    """
     def __init__(self, action_dim):
         super(DiagGaussianDistribution, self).__init__()
         self.distribution = None
@@ -53,12 +60,29 @@ class DiagGaussianDistribution(Distribution):
         self.log_std = None
 
     def proba_distribution_net(self, latent_dim, log_std_init=0.0):
+        """
+        Create the layers and parameter that represent the distribution:
+        one output will be the mean of the gaussian, the other parameter will be the
+        standard deviation (log std in fact to allow negative values)
+
+        :param latent_dim: (int) Dimension og the last layer of the policy (before the action layer)
+        :param log_std_init: (float) Initial value for the log standard deviation
+        :return: (nn.Linear, nn.Parameter)
+        """
         mean_actions = nn.Linear(latent_dim, self.action_dim)
         # TODO: allow action dependent std
         log_std = nn.Parameter(th.ones(self.action_dim) * log_std_init)
         return mean_actions, log_std
 
     def proba_distribution(self, mean_actions, log_std, deterministic=False):
+        """
+        Create and sample for the distribution given its parameters (mean, std)
+
+        :param mean_actions: (th.Tensor)
+        :param log_std: (th.Tensor)
+        :param deterministic: (bool)
+        :return: (th.Tensor)
+        """
         action_std = th.ones_like(mean_actions) * log_std.exp()
         self.distribution = Normal(mean_actions, action_std)
         if deterministic:
@@ -77,11 +101,27 @@ class DiagGaussianDistribution(Distribution):
         return self.distribution.entropy()
 
     def log_prob_from_params(self, mean_actions, log_std):
+        """
+        Compute the log probabilty of taking an action
+        given the distribution parameters.
+
+        :param mean_actions: (th.Tensor)
+        :param log_std: (th.Tensor)
+        :return: (th.Tensor, th.Tensor)
+        """
         action, _ = self.proba_distribution(mean_actions, log_std)
         log_prob = self.log_prob(action)
         return action, log_prob
 
     def log_prob(self, action):
+        """
+        Get the log probabilty of an action given a distribution.
+        Note that you must call `proba_distribution()` method
+        before.
+
+        :param action: (th.Tensor)
+        :return: (th.Tensor)
+        """
         log_prob = self.distribution.log_prob(action)
         if len(log_prob.shape) > 1:
             log_prob = log_prob.sum(axis=1)
@@ -91,6 +131,13 @@ class DiagGaussianDistribution(Distribution):
 
 
 class SquashedDiagGaussianDistribution(DiagGaussianDistribution):
+    """
+    Gaussian distribution with diagonal covariance matrix,
+    followed by a squashing function (tanh) to ensure bounds.
+
+    :param action_dim: (int) Number of continuous actions
+    :param epsilon: (float) small value to avoid NaN due to numerical imprecision.
+    """
     def __init__(self, action_dim, epsilon=1e-6):
         super(SquashedDiagGaussianDistribution, self).__init__(action_dim)
         # Avoid NaN (prevents division by zero or log of zero)
@@ -118,25 +165,40 @@ class SquashedDiagGaussianDistribution(DiagGaussianDistribution):
 
     def log_prob(self, action, gaussian_action=None):
         # Inverse tanh
-        # Naive implementation (not stable): 0.5 * torch.log((1 + x ) / (1 - x))
+        # Naive implementation (not stable): 0.5 * torch.log((1 + x) / (1 - x))
         # We use numpy to avoid numerical instability
         if gaussian_action is None:
-            gaussian_action = th.from_numpy(np.arctanh(action.cpu().numpy())).to(action.device)
+            # It will be clipped to avoid NaN when inversing tanh
+            gaussian_action = TanhBijector.inverse(action)
 
         # Log likelihood for a gaussian distribution
         log_prob = super(SquashedDiagGaussianDistribution, self).log_prob(gaussian_action)
         # Squash correction (from original SAC implementation)
+        # this comes from the fact that tanh is bijective and differentiable
         log_prob -= th.sum(th.log(1 - action ** 2 + self.epsilon), dim=1)
         return log_prob
 
 
 class CategoricalDistribution(Distribution):
+    """
+    Categorical distribution for discrete actions.
+
+    :param action_dim: (int) Number of discrete actions
+    """
     def __init__(self, action_dim):
         super(CategoricalDistribution, self).__init__()
         self.distribution = None
         self.action_dim = action_dim
 
     def proba_distribution_net(self, latent_dim):
+        """
+        Create the layer that represents the distribution:
+        it will be the logits of the Categorical distribution.
+        You can then get probabilties using a softmax.
+
+        :param latent_dim: (int) Dimension og the last layer of the policy (before the action layer)
+        :return: (nn.Linear)
+        """
         action_logits = nn.Linear(latent_dim, self.action_dim)
         return action_logits
 
@@ -167,15 +229,195 @@ class CategoricalDistribution(Distribution):
         return log_prob
 
 
-def make_proba_distribution(action_space):
+class StateDependentNoiseDistribution(Distribution):
+    """
+    Distribution class for using State Dependent Exploration (SDE).
+    It is used to create the noise exploration matrix and
+    compute the log probabilty of an action with that noise.
+
+    :param action_dim: (int) Number of continuous actions
+    :param use_expln: (bool) Use `expln()` function instead of `exp()` 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.
+    :param squash_output: (bool) Whether to squash the output using a tanh function,
+        this allows to ensure boundaries.
+    :param epsilon: (float) small value to avoid NaN due to numerical imprecision.
+    """
+    def __init__(self, action_dim, use_expln=False,
+                 squash_output=False, epsilon=1e-6):
+        super(StateDependentNoiseDistribution, self).__init__()
+        self.distribution = None
+        self.action_dim = action_dim
+        self.mean_actions = None
+        self.log_std = None
+        self.weights_dist = None
+        self.exploration_mat = None
+        self.use_expln = use_expln
+        if squash_output:
+            print("== Using TanhBijector ===")
+            self.bijector = TanhBijector(epsilon)
+        else:
+            self.bijector = None
+
+    def get_std(self, log_std):
+        """
+        Get the standard deviation from the learned parameter
+        (log of it by default). This ensures that the std is positive.
+
+        :param log_std: (th.Tensor)
+        :return: (th.Tensor)
+        """
+        if self.use_expln:
+            # From SDE paper, it allows to keep variance
+            # above zero and prevent it from growing too fast
+            if log_std <= 0:
+                return th.exp(log_std)
+            else:
+                return th.log(log_std + 1.0) + 1.0
+        else:
+            # Use normal exponential
+            return th.exp(log_std)
+
+    def sample_weights(self, log_std):
+        """
+        Sample weights for the noise exploration matrix,
+        using a centered gaussian distribution.
+
+        :param log_std: (th.Tensor)
+        """
+        # TODO: reduce the number of learned dimensions (cf TD3)
+        self.weights_dist = Normal(th.zeros_like(log_std), self.get_std(log_std))
+        self.exploration_mat = self.weights_dist.rsample()
+
+    def proba_distribution_net(self, latent_dim, log_std_init=0.0):
+        """
+        Create the layers and parameter that represent the distribution:
+        one output will be the deterministic action, the other parameter will be the
+        standard deviation of the distribution that control the weights of the noise matrix.
+
+        :param latent_dim: (int) Dimension og the last layer of the policy (before the action layer)
+        :param log_std_init: (float) Initial value for the log standard deviation
+        :return: (nn.Linear, nn.Parameter)
+        """
+        mean_actions = nn.Linear(latent_dim, self.action_dim)
+        log_std = nn.Parameter(th.ones(latent_dim, self.action_dim) * log_std_init)
+        self.sample_weights(log_std)
+        return mean_actions, log_std
+
+    def proba_distribution(self, mean_actions, log_std, latent_pi, deterministic=False):
+        """
+        Create and sample for the distribution given its parameters (mean, std)
+
+        :param mean_actions: (th.Tensor)
+        :param log_std: (th.Tensor)
+        :param deterministic: (bool)
+        :return: (th.Tensor)
+        """
+        variance = th.mm(latent_pi.detach() ** 2, self.get_std(log_std) ** 2)
+        self.distribution = Normal(mean_actions, th.sqrt(variance))
+
+        if deterministic:
+            action = self.mode()
+        else:
+            action = self.sample(latent_pi)
+        return action, self
+
+    def mode(self):
+        action = self.distribution.mean
+        if self.bijector is not None:
+            return self.bijector.forward(action)
+        return action
+
+    def sample(self, latent_pi):
+        noise = th.mm(latent_pi.detach(), self.exploration_mat)
+        action = self.distribution.mean + noise
+        if self.bijector is not None:
+            return self.bijector.forward(action)
+        return action
+
+    def entropy(self):
+        # TODO: account for the squashing?
+        return self.distribution.entropy()
+
+    def log_prob_from_params(self, mean_actions, log_std, latent_pi):
+        action, _ = self.proba_distribution(mean_actions, log_std, latent_pi)
+        log_prob = self.log_prob(action)
+        return action, log_prob
+
+    def log_prob(self, action):
+        if self.bijector is not None:
+            gaussian_action = self.bijector.inverse(action)
+        else:
+            gaussian_action = action
+        # log likelihood for a gaussian
+        log_prob = self.distribution.log_prob(gaussian_action)
+
+        if len(log_prob.shape) > 1:
+            log_prob = log_prob.sum(axis=1)
+        else:
+            log_prob = log_prob.sum()
+
+        if self.bijector is not None:
+            # Squash correction (from original SAC implementation)
+            log_prob -= th.sum(self.bijector.log_prob_correction(gaussian_action), dim=1)
+        return log_prob
+
+
+class TanhBijector(object):
+    """
+    Bijective transformation of a probabilty distribution
+    using a squashing function (tanh)
+    TODO: use Pyro instead (https://pyro.ai/)
+
+    :param epsilon: (float) small value to avoid NaN due to numerical imprecision.
+    """
+    def __init__(self, epsilon=1e-6):
+        super(TanhBijector, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        return th.tanh(x)
+
+    @staticmethod
+    def atanh(x):
+        """
+        Inverse of Tanh
+
+        Taken from pyro: https://github.com/pyro-ppl/pyro
+        0.5 * torch.log((1 + x ) / (1 - x))
+        """
+        return 0.5 * (x.log1p() - (-x).log1p())
+
+    @staticmethod
+    def inverse(y):
+        """
+        Inverse tanh.
+
+        :param y: (th.Tensor)
+        :return: (th.Tensor)
+        """
+        eps = th.finfo(y.dtype).eps
+        # Clip the action to avoid NaN
+        return TanhBijector.atanh(y.clamp(min=-1. + eps, max=1. - eps))
+
+    def log_prob_correction(self, x):
+        # Squash correction (from original SAC implementation)
+        return th.log(1 - th.tanh(x) ** 2 + self.epsilon)
+
+
+def make_proba_distribution(action_space, use_sde=False):
     """
     Return an instance of Distribution for the correct type of action space
 
     :param action_space: (Gym Space) the input action space
+    :param use_sde: (bool) Force the use of StateDependentNoiseDistribution
+        instead of DiagGaussianDistribution
     :return: (Distribution) the approriate Distribution object
     """
     if isinstance(action_space, spaces.Box):
         assert len(action_space.shape) == 1, "Error: the action space must be a vector"
+        if use_sde:
+            return StateDependentNoiseDistribution(action_space.shape[0])
         return DiagGaussianDistribution(action_space.shape[0])
     elif isinstance(action_space, spaces.Discrete):
         return CategoricalDistribution(action_space.n)

torchy_baselines/common/monitor.py

@@ -13,7 +13,7 @@ class Monitor(Wrapper):
     EXT = "monitor.csv"
     file_handler = None
 
-    def __init__(self, env, filename, allow_early_resets=True, reset_keywords=(), info_keywords=()):
+    def __init__(self, env, filename=None, allow_early_resets=True, reset_keywords=(), info_keywords=()):
         """
         A monitor wrapper for Gym environments, it is used to know the episode reward, length, time and other data.
 

torchy_baselines/ppo/policies.py

@@ -6,7 +6,8 @@ import torch.nn as nn
 import numpy as np
 
 from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp
-from torchy_baselines.common.distributions import make_proba_distribution, DiagGaussianDistribution, CategoricalDistribution
+from torchy_baselines.common.distributions import make_proba_distribution,\
+    DiagGaussianDistribution, CategoricalDistribution, StateDependentNoiseDistribution
 
 
 class MlpExtractor(nn.Module):
@@ -101,7 +102,8 @@ class MlpExtractor(nn.Module):
 class PPOPolicy(BasePolicy):
     def __init__(self, observation_space, action_space,
                  learning_rate, net_arch=None, device='cpu',
-                 activation_fn=nn.Tanh, adam_epsilon=1e-5, ortho_init=True):
+                 activation_fn=nn.Tanh, adam_epsilon=1e-5,
+                 ortho_init=True, use_sde=False, log_std_init=0.0):
         super(PPOPolicy, self).__init__(observation_space, action_space, device)
         self.obs_dim = self.observation_space.shape[0]
         if net_arch is None:
@@ -118,21 +120,25 @@ class PPOPolicy(BasePolicy):
         }
         self.shared_net = None
         self.pi_net, self.vf_net = None, None
-        # Action distribution
-        self.action_dist = make_proba_distribution(action_space)
         # In the future, feature_extractor will be replaced with a CNN
         self.features_extractor = nn.Flatten()
         self.features_dim = self.obs_dim
+        self.log_std_init = log_std_init
+        # Action distribution
+        self.action_dist = make_proba_distribution(action_space, use_sde=use_sde)
+
         self._build(learning_rate)
 
+    def reset_noise_net(self):
+        self.action_dist.sample_weights(self.log_std)
+
     def _build(self, learning_rate):
         self.mlp_extractor = MlpExtractor(self.features_dim, net_arch=self.net_arch,
                                           activation_fn=self.activation_fn, device=self.device)
 
-        # self.action_net = nn.Linear(self.net_arch[-1], self.action_dim)
-        # self.log_std = nn.Parameter(th.zeros(self.action_dim))
-        if isinstance(self.action_dist, DiagGaussianDistribution):
-            self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=self.mlp_extractor.latent_dim_pi)
+        if isinstance(self.action_dist, (DiagGaussianDistribution, StateDependentNoiseDistribution)):
+            self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=self.mlp_extractor.latent_dim_pi,
+                                                                                    log_std_init=self.log_std_init)
         elif isinstance(self.action_dist, CategoricalDistribution):
             self.action_net = self.action_dist.proba_distribution_net(latent_dim=self.mlp_extractor.latent_dim_pi)
 
@@ -162,21 +168,26 @@ class PPOPolicy(BasePolicy):
     def _get_latent(self, obs):
         return self.mlp_extractor(self.features_extractor(obs))
 
-    def _get_action_dist_from_latent(self, latent, deterministic=False):
-        mean_actions = self.action_net(latent)
+    def _get_action_dist_from_latent(self, latent_pi, deterministic=False):
+        mean_actions = self.action_net(latent_pi)
+
         if isinstance(self.action_dist, DiagGaussianDistribution):
             return self.action_dist.proba_distribution(mean_actions, self.log_std, deterministic=deterministic)
+
         elif isinstance(self.action_dist, CategoricalDistribution):
             return self.action_dist.proba_distribution(mean_actions, deterministic=deterministic)
 
+        elif isinstance(self.action_dist, StateDependentNoiseDistribution):
+            return self.action_dist.proba_distribution(mean_actions, self.log_std, latent_pi, deterministic=deterministic)
+
     def actor_forward(self, obs, deterministic=False):
         latent_pi, _ = self._get_latent(obs)
         action, _ = self._get_action_dist_from_latent(latent_pi, deterministic=deterministic)
         return action.detach().cpu().numpy()
 
-    def get_policy_stats(self, obs, action):
+    def get_policy_stats(self, obs, action, deterministic=False):
         latent_pi, latent_vf = self._get_latent(obs)
-        _, action_distribution = self._get_action_dist_from_latent(latent_pi)
+        _, action_distribution = self._get_action_dist_from_latent(latent_pi, deterministic=deterministic)
         log_prob = action_distribution.log_prob(action)
         value = self.value_net(latent_vf)
         return value, log_prob, action_distribution.entropy()

torchy_baselines/ppo/ppo.py

@@ -18,7 +18,7 @@ from torchy_baselines.common.base_class import BaseRLModel
 from torchy_baselines.common.evaluation import evaluate_policy
 from torchy_baselines.common.buffers import RolloutBuffer
 from torchy_baselines.common.utils import explained_variance, get_schedule_fn
-from torchy_baselines.common.vec_env import VecNormalize
+from torchy_baselines.common.vec_env import VecNormalize, VecEnvWrapper
 from torchy_baselines.common import logger
 from torchy_baselines.ppo.policies import PPOPolicy
 
@@ -53,6 +53,8 @@ class PPO(BaseRLModel):
     :param ent_coef: (float) Entropy coefficient for the loss calculation
     :param vf_coef: (float) Value function coefficient for the loss calculation
     :param max_grad_norm: (float) The maximum value for the gradient clipping
+    :param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
+        instead of action noise exploration (default: False)
     :param target_kl: (float) Limit the KL divergence between updates,
         because the clipping is not enough to prevent large update
         see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213)
@@ -71,7 +73,7 @@ class PPO(BaseRLModel):
     def __init__(self, policy, env, learning_rate=3e-4,
                  n_steps=2048, batch_size=64, n_epochs=10,
                  gamma=0.99, gae_lambda=0.95, clip_range=0.2, clip_range_vf=None,
-                 ent_coef=0.0, vf_coef=0.5, max_grad_norm=0.5,
+                 ent_coef=0.0, vf_coef=0.5, max_grad_norm=0.5, use_sde=False,
                  target_kl=None, tensorboard_log=None, create_eval_env=False,
                  policy_kwargs=None, verbose=0, seed=0, device='auto',
                  _init_setup_model=True):
@@ -95,6 +97,7 @@ class PPO(BaseRLModel):
         self.target_kl = target_kl
         self.tensorboard_log = tensorboard_log
         self.tb_writer = None
+        self.use_sde = use_sde
 
         if _init_setup_model:
             self._setup_model()
@@ -117,19 +120,20 @@ class PPO(BaseRLModel):
         self.rollout_buffer = RolloutBuffer(self.n_steps, state_dim, action_dim, self.device,
                                             gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs)
         self.policy = self.policy(self.observation_space, self.action_space,
-                                  self.learning_rate, device=self.device, **self.policy_kwargs)
+                                  self.learning_rate, use_sde=self.use_sde, device=self.device,
+                                  **self.policy_kwargs)
         self.policy = self.policy.to(self.device)
 
         self.clip_range = get_schedule_fn(self.clip_range)
         if self.clip_range_vf is not None:
             self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
 
-    def select_action(self, observation,deterministic=False):
+    def select_action(self, observation, deterministic=False):
         # Normally not needed
         observation = np.array(observation)
         with th.no_grad():
             observation = th.FloatTensor(observation.reshape(1, -1)).to(self.device)
-            return self.policy.actor_forward(observation, deterministic)
+            return self.policy.actor_forward(observation, deterministic=deterministic)
 
     def predict(self, observation, state=None, mask=None, deterministic=False):
         """
@@ -141,7 +145,7 @@ class PPO(BaseRLModel):
         :param deterministic: (bool) Whether or not to return deterministic actions.
         :return: (np.ndarray, np.ndarray) the model's action and the next state (used in recurrent policies)
         """
-        clipped_actions = self.select_action(observation)
+        clipped_actions = self.select_action(observation, deterministic=deterministic)
         if isinstance(self.action_space, gym.spaces.Box):
             clipped_actions = np.clip(clipped_actions, self.action_space.low, self.action_space.high)
         return clipped_actions
@@ -151,6 +155,10 @@ class PPO(BaseRLModel):
 
         n_steps = 0
         rollout_buffer.reset()
+        # Sample new weights for the state dependent exploration
+        # TODO: ensure episodic setting?
+        if self.use_sde:
+            self.policy.reset_noise_net()
 
         while n_steps < n_rollout_steps:
             with th.no_grad():
@@ -219,6 +227,7 @@ class PPO(BaseRLModel):
                 # Value loss using the TD(gae_lambda) target
                 value_loss = F.mse_loss(return_batch, values_pred)
 
+
                 # Entropy loss favor exploration
                 entropy_loss = -th.mean(entropy)
 
@@ -233,11 +242,19 @@ class PPO(BaseRLModel):
                 approx_kl_divs.append(th.mean(old_log_prob - log_prob).detach().cpu().numpy())
 
             if self.target_kl is not None and np.mean(approx_kl_divs) > 1.5 * self.target_kl:
-                print("Early stopping at step {} due to reaching max kl: {:.2f}".format(gradient_step, np.mean(approx_kl_divs)))
+                print("Early stopping at step {} due to reaching max kl: {:.2f}".format(it, np.mean(approx_kl_divs)))
                 break
 
-        # print(explained_variance(self.rollout_buffer.returns.flatten().cpu().numpy(),
-        #                          self.rollout_buffer.values.flatten().cpu().numpy()))
+        explained_var = explained_variance(self.rollout_buffer.returns.flatten().cpu().numpy(),
+                                           self.rollout_buffer.values.flatten().cpu().numpy())
+
+        logger.logkv("explained_variance", explained_var)
+        # TODO: gather stats for the entropy and other losses?
+        logger.logkv("entropy", entropy.mean().item())
+        logger.logkv("policy_loss", policy_loss.item())
+        logger.logkv("value_loss", value_loss.item())
+        if hasattr(self.policy, 'log_std'):
+            logger.logkv("std", th.exp(self.policy.log_std).mean().item())
 
     def learn(self, total_timesteps, callback=None, log_interval=1,
               eval_env=None, eval_freq=-1, n_eval_episodes=5, tb_log_name="PPO", reset_num_timesteps=True):
@@ -278,9 +295,14 @@ class PPO(BaseRLModel):
             # Evaluate agent
             if 0 < eval_freq <= timesteps_since_eval and eval_env is not None:
                 timesteps_since_eval %= eval_freq
+                # TODO: move that to the base class
                 # Sync eval env and train env when using VecNormalize
-                if isinstance(self.env, VecNormalize):
-                    eval_env.obs_rms = deepcopy(self.env.obs_rms)
+                env_tmp, eval_env_tmp = self.env, eval_env
+                while isinstance(env_tmp, VecEnvWrapper):
+                    if isinstance(env_tmp, VecNormalize):
+                        eval_env_tmp.obs_rms = deepcopy(env_tmp.obs_rms)
+                    env_tmp = env_tmp.venv
+                    eval_env_tmp.venv
                 mean_reward, _ = evaluate_policy(self, eval_env, n_eval_episodes)
                 if self.tb_writer is not None:
                     self.tb_writer.add_scalar('Eval/reward', mean_reward, self.num_timesteps)