Fixed shared_lstm argument in CNN and MultiInput Policies for RecurrentPPO (#90)

* fixed shared_lstm parameter in CNN and MultiInput Policies

* updated tests

* changelog

* Fix FPS for recurrent PPO

* Fix import

* Update changelog

Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 1.6.1a0 (WIP)
+Release 1.6.1a1 (WIP)
 -------------------------------
 
 Breaking Changes:
@@ -15,8 +15,10 @@ New Features:
 
 Bug Fixes:
 ^^^^^^^^^^
+- Fixed the issue of wrongly passing policy arguments when using CnnLstmPolicy or MultiInputLstmPolicy with ``RecurrentPPO`` (@mlodel)
 - Fixed division by zero error when computing FPS when a small number of time has elapsed in operating systems with low-precision timers.
 
+
 Deprecations:
 ^^^^^^^^^^^^^
 
@@ -294,3 +296,4 @@ Contributors:
 -------------
 
 @ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar @rnederstigt @qgallouedec
+@mlodel

sb3_contrib/common/recurrent/policies.py

@@ -483,6 +483,7 @@ class RecurrentActorCriticCnnPolicy(RecurrentActorCriticPolicy):
         optimizer_kwargs: Optional[Dict[str, Any]] = None,
         lstm_hidden_size: int = 256,
         n_lstm_layers: int = 1,
+        shared_lstm: bool = False,
         enable_critic_lstm: bool = True,
         lstm_kwargs: Optional[Dict[str, Any]] = None,
     ):
@@ -506,6 +507,7 @@ class RecurrentActorCriticCnnPolicy(RecurrentActorCriticPolicy):
             optimizer_kwargs,
             lstm_hidden_size,
             n_lstm_layers,
+            shared_lstm,
             enable_critic_lstm,
             lstm_kwargs,
         )
@@ -573,6 +575,7 @@ class RecurrentMultiInputActorCriticPolicy(RecurrentActorCriticPolicy):
         optimizer_kwargs: Optional[Dict[str, Any]] = None,
         lstm_hidden_size: int = 256,
         n_lstm_layers: int = 1,
+        shared_lstm: bool = False,
         enable_critic_lstm: bool = True,
         lstm_kwargs: Optional[Dict[str, Any]] = None,
     ):
@@ -596,6 +599,7 @@ class RecurrentMultiInputActorCriticPolicy(RecurrentActorCriticPolicy):
             optimizer_kwargs,
             lstm_hidden_size,
             n_lstm_layers,
+            shared_lstm,
             enable_critic_lstm,
             lstm_kwargs,
         )

sb3_contrib/ppo_recurrent/ppo_recurrent.py

@@ -1,3 +1,4 @@
+import sys
 import time
 from copy import deepcopy
 from typing import Any, Dict, Optional, Tuple, Type, Union
@@ -513,13 +514,14 @@ class RecurrentPPO(OnPolicyAlgorithm):
 
             # Display training infos
             if log_interval is not None and iteration % log_interval == 0:
-                fps = int((self.num_timesteps - self._num_timesteps_at_start) / (time.time() - self.start_time))
+                time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
+                fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
                 self.logger.record("time/iterations", iteration, exclude="tensorboard")
                 if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
                     self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
                     self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
                 self.logger.record("time/fps", fps)
-                self.logger.record("time/time_elapsed", int(time.time() - self.start_time), exclude="tensorboard")
+                self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
                 self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
                 self.logger.dump(step=self.num_timesteps)
 

sb3_contrib/version.txt

@@ -1 +1 @@
-1.6.1a0
+1.6.1a1

tests/test_lstm.py

@@ -57,13 +57,30 @@ class CartPoleNoVelEnv(CartPoleEnv):
         return CartPoleNoVelEnv._pos_obs(full_obs), rew, done, info
 
 
-def test_cnn():
+@pytest.mark.parametrize(
+    "policy_kwargs",
+    [
+        {},
+        dict(shared_lstm=True, enable_critic_lstm=False),
+        dict(
+            enable_critic_lstm=True,
+            lstm_hidden_size=4,
+            lstm_kwargs=dict(dropout=0.5),
+        ),
+        dict(
+            enable_critic_lstm=False,
+            lstm_hidden_size=4,
+            lstm_kwargs=dict(dropout=0.5),
+        ),
+    ],
+)
+def test_cnn(policy_kwargs):
     model = RecurrentPPO(
         "CnnLstmPolicy",
         FakeImageEnv(screen_height=40, screen_width=40, n_channels=3),
         n_steps=16,
         seed=0,
-        policy_kwargs=dict(features_extractor_kwargs=dict(features_dim=32)),
+        policy_kwargs=dict(**policy_kwargs, features_extractor_kwargs=dict(features_dim=32)),
     )
 
     model.learn(total_timesteps=32)
@@ -138,9 +155,26 @@ def test_run_sde():
     model.learn(total_timesteps=200, eval_freq=150)
 
 
-def test_dict_obs():
+@pytest.mark.parametrize(
+    "policy_kwargs",
+    [
+        {},
+        dict(shared_lstm=True, enable_critic_lstm=False),
+        dict(
+            enable_critic_lstm=True,
+            lstm_hidden_size=4,
+            lstm_kwargs=dict(dropout=0.5),
+        ),
+        dict(
+            enable_critic_lstm=False,
+            lstm_hidden_size=4,
+            lstm_kwargs=dict(dropout=0.5),
+        ),
+    ],
+)
+def test_dict_obs(policy_kwargs):
     env = make_vec_env("CartPole-v1", n_envs=1, wrapper_class=ToDictWrapper)
-    model = RecurrentPPO("MultiInputLstmPolicy", env, n_steps=32).learn(64)
+    model = RecurrentPPO("MultiInputLstmPolicy", env, n_steps=32, policy_kwargs=policy_kwargs).learn(64)
     evaluate_policy(model, env, warn=False)