added set_env test and set_env wrapping

tests/test_save_load.py

@@ -7,7 +7,7 @@ import torch as th
 
 from torchy_baselines import A2C, CEMRL, PPO, SAC, TD3
 from torchy_baselines.common.vec_env import DummyVecEnv
-from torchy_baselines.common.identity_env import IdentityEnvBox
+from torchy_baselines.common.identity_env import IdentityEnvBox, IdentityEnv
 
 MODEL_LIST = [
     CEMRL,
@@ -101,7 +101,28 @@ def test_save_load(model_class):
 
 @pytest.mark.parametrize("model_class", MODEL_LIST)
 def test_set_env(model_class):
-    pass
+    """
+    Test if set_env function does work correct
+    :param model_class: (BaseRLModel) A RL model
+    """
+    env = DummyVecEnv([lambda: IdentityEnvBox(10)])
+    env2 = DummyVecEnv([lambda: IdentityEnvBox(10)])
+    env3 = IdentityEnvBox(10)
+
+    # create model
+    model = model_class('MlpPolicy', env, policy_kwargs=dict(net_arch=[16]), create_eval_env=True)
+    # learn
+    model.learn(total_timesteps=1000, eval_freq=500)
+
+    # change env
+    model.set_env(env2)
+    # learn again
+    model.learn(total_timesteps=1000, eval_freq=500)
+
+    # change env test wrapping
+    model.set_env(env3)
+    # learn again
+    model.learn(total_timesteps=1000, eval_freq=500)
 
 
 @pytest.mark.parametrize("model_class", MODEL_LIST)

torchy_baselines/common/base_class.py

@@ -185,6 +185,7 @@ class BaseRLModel(object):
     def set_env(self, env):
         """
         Checks the validity of the environment, and if it is coherent, set it as the current environment.
+        Furthermore wrap any non vectorized env into a vectorized
         checked parameters:
          - observation_space
          - action_space
@@ -193,13 +194,16 @@ class BaseRLModel(object):
         """
         if self.check_env(env, self.observation_space, self.action_space) is False:
             raise ValueError("Given environment is not compatible with model")
-        # if all fits save new env
+        # it must be coherent now
+        # if it is not a VecEnv, make it a VecEnv
+        if not isinstance(env, VecEnv):
+            if self.verbose >= 1:
+                print("Wrapping the env in a DummyVecEnv.")
+            env = DummyVecEnv([lambda: env])
+        self.n_envs = env.num_envs
         self.env = env
-        # and update observation and action space
-        self.observation_space = env.observation_space
-        self.action_space = env.action_space
 
-    def get_parameter_list(self):
+    def get_parameters(self):
         """
         Returns policy and optimizer parameters as a tuple
         :return: (dict,dict) policy_parameters, opt_parameters
@@ -540,7 +544,8 @@ class BaseRLModel(object):
                     with archive.open(file_name + '.pth', mode="w") as opt_param_file:
                         th.save(dict, opt_param_file)
 
-    def excluded_save_params(self):
+    @staticmethod
+    def excluded_save_params():
         """
         returns the names of the parameters that should be excluded from save
         :return: ([str]) List of parameters that should be excluded from save