diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py b/freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
index 1b2873334..c00784d7a 100644
--- a/freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
+++ b/freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
@@ -50,19 +50,22 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
         test_df = data_dictionary["test_features"]
         eval_freq = agent_params.get("eval_cycles", 4) * len(test_df)
         total_timesteps = agent_params["train_cycles"] * len(train_df)
+        learning_rate = agent_params["learning_rate"]
 
         # price data for model training and evaluation
         price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
         price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
             len(test_df.index))
 
-        env_id = "CartPole-v1"
-        num_cpu = 4
+        env_id = "train_env"
+        train_num_cpu = 6
         train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
-                                   self.CONV_WIDTH) for i in range(num_cpu)])
+                                   self.CONV_WIDTH) for i in range(train_num_cpu)])
 
-        eval_env = SubprocVecEnv([make_env(env_id, i, 1, test_df, price_test, reward_params,
-                                  self.CONV_WIDTH) for i in range(num_cpu)])
+        eval_num_cpu = 6
+        eval_env_id = 'eval_env'
+        eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
+                                  self.CONV_WIDTH) for i in range(eval_num_cpu)])
 
         path = self.dk.data_path
         eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
@@ -71,10 +74,10 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
 
         # model arch
         policy_kwargs = dict(activation_fn=th.nn.ReLU,
-                             net_arch=[256, 256, 128])
+                             net_arch=[512, 512, 512])
 
         model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
-                    tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=0.00025, gamma=0.9, verbose=1
+                    tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=learning_rate, gamma=0.9, verbose=1
                     )
 
         model.learn(
@@ -83,6 +86,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
         )
 
         print('Training finished!')
+        eval_env.close()
 
         return model