Merge branch 'main' into rtd/automodule

.github/workflows/ci.yml

@@ -62,7 +62,6 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install setuptools==65.5.0
         pip install flake8 pytest
         pip install -r requirements.txt
         
@@ -98,7 +97,6 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install setuptools==65.5.0
         pip install flake8 pytest
         pip install -r requirements.txt
         pip install sphinx
@@ -131,7 +129,6 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install setuptools==65.5.0
         pip install flake8 pytest
         pip install -r requirements.txt
 
@@ -160,7 +157,6 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install setuptools==65.5.0
         pip install flake8 pytest
         pip install -r requirements.txt
 

doc/rtd/content/01_welcome/sub/deps.txt

@@ -3,8 +3,7 @@ numpy,1.24.2
 torch,2.0.0
 matplotlib,3.7.1
 transformations,2022.9.26
-stable-baselines3,1.8.0
-gym,0.21.0
+stable_baselines3,2.0.0a1
 scipy,1.10.1
 pettingzoo,1.22.3
 pygame,2.1.3

doc/rtd/requirements.txt

@@ -3,8 +3,7 @@ numpy>=1.24.2
 torch>=2.0.0
 matplotlib>=3.7.1
 transformations>=2022.9.26
-stable-baselines3>=1.8.0
-gym>=0.21.0
+stable_baselines3>=2.0.0a1
 scipy>=1.10.1
 pettingzoo>=1.22.3
 pygame>=2.1.3

requirements.txt

@@ -3,8 +3,7 @@ numpy>=1.24.2
 torch>=2.0.0
 matplotlib>=3.7.1
 transformations>=2022.9.26
-stable-baselines3>=1.8.0
-gym>=0.21.0
+stable_baselines3>=2.0.0a1
 scipy>=1.10.1
 pettingzoo>=1.22.3
 pygame>=2.1.3

setup.cfg

@@ -31,8 +31,7 @@ full =
     torch>=2.0.0
     matplotlib>=3.7.1
     transformations>=2022.9.26
-    stable-baselines3>=1.8.0
-    gym>=0.21.0
+    stable_baselines3>=2.0.0a1
     scipy>=1.10.1
     pettingzoo>=1.22.3
     pygame>=2.1.3

src/mlpro/rl/examples/howto_rl_agent_001_run_agent_with_own_policy_on_gym_environment.py

@@ -21,10 +21,11 @@
 ## -- 2022-11-02  1.2.6     DA       Refactoring 
 ## -- 2022-11-07  1.3.0     DA       Refactoring 
 ## -- 2023-01-14  1.3.1     MRD      Removing default parameter new_step_api and render_mode for gym
+## -- 2023-04-19  1.3.2     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.3.1 (2023-01-14)
+Ver. 1.3.2 (2023-04-19)
 
 This module shows how to run an own policy inside the standard agent model with an OpenAI Gym environment using 
 MLPro framework.
@@ -46,8 +47,8 @@
 
 from mlpro.bf.math import *
 from mlpro.rl import *
-from mlpro.wrappers.openai_gym import WrEnvGYM2MLPro
-import gym
+from mlpro.wrappers.gymnasium import WrEnvGYM2MLPro
+import gymnasium as gym
 import random
 
 
@@ -91,7 +92,10 @@ class MyScenario (RLScenario):
 
     def _setup(self, p_mode, p_ada: bool, p_visualize:bool, p_logging) -> Model:
         # 2.1 Setup environment
-        gym_env     = gym.make('CartPole-v1')
+        if p_visualize:
+            gym_env     = gym.make('CartPole-v1', render_mode="human")
+        else:
+            gym_env     = gym.make('CartPole-v1')
         self._env   = WrEnvGYM2MLPro( p_gym_env=gym_env, p_visualize=p_visualize, p_logging=p_logging) 
 
         # 2.2 Setup standard single-agent with own policy

src/mlpro/rl/examples/howto_rl_agent_002_train_agent_with_own_policy_on_gym_environment.py

@@ -24,10 +24,11 @@
 ## -- 2022-11-02  1.3.6     DA       Refactoring 
 ## -- 2022-11-07  1.4.0     DA       Refactoring 
 ## -- 2023-01-14  1.4.1     MRD      Removing default parameter new_step_api and render_mode for gym
+## -- 2023-04-19  1.4.2     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.4.1 (2023-01-14)
+Ver. 1.4.2 (2023-04-19)
 
 This module shows how to train an agent with a custom policy inside on an OpenAI Gym environment using
 MLPro framework.
@@ -49,8 +50,8 @@
 
 from mlpro.bf.math import *
 from mlpro.rl import *
-from mlpro.wrappers.openai_gym import WrEnvGYM2MLPro
-import gym
+from mlpro.wrappers.gymnasium import WrEnvGYM2MLPro
+import gymnasium as gym
 import random
 from pathlib import Path
 
@@ -95,7 +96,10 @@ class MyScenario (RLScenario):
 
     def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging) -> Model:
         # 2.1 Setup environment
-        gym_env     = gym.make('CartPole-v1')
+        if p_visualize:
+            gym_env     = gym.make('CartPole-v1', render_mode="human")
+        else:
+            gym_env     = gym.make('CartPole-v1')
         self._env   = WrEnvGYM2MLPro(gym_env, p_visualize=p_visualize, p_logging=p_logging) 
 
         # 2.2 Setup and return standard single-agent with own policy

src/mlpro/rl/examples/howto_rl_agent_011_train_and_reload_single_agent_gym.py

@@ -22,10 +22,11 @@
 ## -- 2023-03-02  1.2.1     LSB      Refactoring
 ## -- 2023-03-04  1.3.0     DA       Renamed
 ## -- 2023-03-27  1.3.1     DA       Refactoring
+## -- 2023-04-19  1.3.2     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.3.1 (2023-03-27)
+Ver. 1.3.2 (2023-04-19)
 
 This module shows how to train a single agent and load it again to do some extra cycles.
 
@@ -40,10 +41,10 @@
 """
 
 
-import gym
+import gymnasium as gym
 from stable_baselines3 import PPO
 from mlpro.rl import *
-from mlpro.wrappers.openai_gym import WrEnvGYM2MLPro
+from mlpro.wrappers.gymnasium import WrEnvGYM2MLPro
 from mlpro.wrappers.sb3 import WrPolicySB32MLPro
 from pathlib import Path
 
@@ -55,7 +56,10 @@ class MyScenario (RLScenario):
 
     def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging) -> Model:
         # 1.1 Setup environment
-        gym_env = gym.make('CartPole-v1')
+        if p_visualize:
+            gym_env     = gym.make('CartPole-v1', render_mode="human")
+        else:
+            gym_env     = gym.make('CartPole-v1')
         self._env = WrEnvGYM2MLPro(gym_env, p_visualize=p_visualize, p_logging=p_logging)
 
         # 1.2 Setup Policy From SB3

src/mlpro/rl/examples/howto_rl_att_001_train_and_reload_single_agent_gym_sd.py

@@ -7,10 +7,11 @@
 ## -- yyyy-mm-dd  Ver.      Auth.    Description
 ## -- 2023-03-04  1.0.0     DA       Creation as derivate of howto_rl_agent_011
 ## -- 2023-03-27  1.0.1     DA       Refactoring
+## -- 2023-04-19  1.0.2     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.0.1 (2023-03-27)
+Ver. 1.0.2 (2023-04-19)
 
 As in Howto RL AGENT 011, this module shows how to train a single agent and load it again to do some
 extra cycles. In opposite to howto 011, stagnation detection is used to automatically end the
@@ -29,10 +30,10 @@
 """
 
 
-import gym
+import gymnasium as gym
 from stable_baselines3 import PPO
 from mlpro.rl import *
-from mlpro.wrappers.openai_gym import WrEnvGYM2MLPro
+from mlpro.wrappers.gymnasium import WrEnvGYM2MLPro
 from mlpro.wrappers.sb3 import WrPolicySB32MLPro
 from pathlib import Path
 
@@ -44,7 +45,10 @@ class MyScenario (RLScenario):
 
     def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging) -> Model:
         # 1.1 Setup environment
-        gym_env = gym.make('CartPole-v1')
+        if p_visualize:
+            gym_env     = gym.make('CartPole-v1', render_mode="human")
+        else:
+            gym_env     = gym.make('CartPole-v1')
         self._env = WrEnvGYM2MLPro(gym_env, p_visualize=p_visualize, p_logging=p_logging)
 
         # 1.2 Setup Policy From SB3

src/mlpro/rl/examples/howto_rl_mb_001_train_and_reload_model_based_agent_gym.py

@@ -10,10 +10,11 @@
 ## -- 2023-03-10  1.0.2     SY       Renumbering module
 ## -- 2023-03-10  1.0.3     SY       Refactoring
 ## -- 2023-03-27  1.0.4     DA       Refactoring
+## -- 2023-04-19  1.0.5     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.0.4 (2023-03-27)
+Ver. 1.0.5 (2023-04-19)
 
 This module shows how to train a single agent in MBRL and load it again to do some extra cycles.
 
@@ -32,11 +33,11 @@
 """
 
 
-import gym
+import gymnasium as gym
 import torch
 from stable_baselines3 import PPO
 from mlpro.rl import *
-from mlpro.wrappers.openai_gym import WrEnvGYM2MLPro
+from mlpro.wrappers.gymnasium import WrEnvGYM2MLPro
 from mlpro.wrappers.sb3 import WrPolicySB32MLPro
 from mlpro.sl.pool.afct.fnn.pytorch.mlp import PyTorchMLP
 from pathlib import Path
@@ -57,7 +58,10 @@ class MyScenario (RLScenario):
     def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging) -> Model:
 
         # 1.1 Setup environment
-        gym_env = gym.make('CartPole-v1')
+        if p_visualize:
+            gym_env     = gym.make('CartPole-v1', render_mode="human")
+        else:
+            gym_env     = gym.make('CartPole-v1')
         self._env = WrEnvGYM2MLPro(gym_env, p_visualize=p_visualize, p_logging=p_logging)
         self._env.reset()
 

src/mlpro/rl/examples/howto_rl_wp_001_mlpro_environment_to_gym_environment.py

@@ -15,10 +15,11 @@
 ## -- 2022-10-14  1.0.6     SY       Refactoring 
 ## -- 2022-11-02  1.0.7     SY       Unable logging in unit test model
 ## -- 2023-03-02  1.0.8     LSB      Refactoring
+## -- 2023-04-19  1.0.9     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.0.8 (2023-03-02)
+Ver. 1.0.9 (2023-04-19)
 
 This module shows how to wrap a native MLPro environment class to OpenAI Gym environment.
 
@@ -31,9 +32,9 @@
 
 
 from mlpro.bf.various import Log
-from mlpro.wrappers.openai_gym import WrEnvMLPro2GYM
+from mlpro.wrappers.gymnasium import WrEnvMLPro2GYM
 from mlpro.rl.pool.envs.gridworld import GridWorld
-from gym.utils.env_checker import check_env
+from gymnasium.utils.env_checker import check_env
 
 
 if __name__ == "__main__":
@@ -48,7 +49,6 @@
 env = WrEnvMLPro2GYM(mlpro_env,
                      p_state_space=None,
                      p_action_space=None,
-                     p_new_step_api=False,
                      p_logging=logging)
 
 # 3. Check whether the environment is valid

src/mlpro/rl/examples/howto_rl_wp_004_train_agent_with_sb3_policy.py

@@ -19,10 +19,11 @@
 ## -- 2022-11-07  1.1.0     DA       Refactoring 
 ## -- 2023-01-14  1.1.1     MRD      Removing default parameter new_step_api and render_mode for gym
 ## -- 2023-02-13  1.1.2     DA       Optimization of dark mode
+## -- 2023-04-19  1.1.3     MRD      Refactor module import gym to gymnasium
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.1.2 (2023-02-13)
+Ver. 1.1.3 (2023-04-19)
 
 This module shows how to train agent with SB3 Wrapper for On- and Off-Policy Algorithms
 
@@ -35,10 +36,10 @@
 """
 
 
-import gym
+import gymnasium as gym
 from stable_baselines3 import A2C, PPO, DQN, DDPG, SAC
 from mlpro.rl.models import *
-from mlpro.wrappers.openai_gym import WrEnvGYM2MLPro
+from mlpro.wrappers.gymnasium import WrEnvGYM2MLPro
 from mlpro.wrappers.sb3 import WrPolicySB32MLPro
 from collections import deque
 from pathlib import Path
@@ -51,7 +52,10 @@ class MyScenario(RLScenario):
     def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging) -> Model:
         # 1 Setup environment
         # self._env   = RobotHTM(p_logging=False)
-        gym_env = gym.make('CartPole-v1')
+        if p_visualize:
+            gym_env     = gym.make('CartPole-v1', render_mode="human")
+        else:
+            gym_env     = gym.make('CartPole-v1')
         self._env = WrEnvGYM2MLPro(gym_env, p_visualize=p_visualize, p_logging=p_logging)
 
         # 2 Instantiate Policy From SB3