Inference uses the same configs as the training and instantiates the model...

Inference uses the same configs as the training and instantiates the model load function from the configs.

cooperative_cuisine/reinforcement_learning/config/additional_configs/additional_config_base.yaml

 order_generator: "random_orders.yaml"
 # Here the filename of the converter should be given. The converter class needs to be called StateConverter and implement the abstract StateToObservationConverter class
 state_converter:
-  _target_: "cooperative_cuisine.reinforcement_learning.obs_converter.base_converter.BaseStateConverter"
+  _target_: "cooperative_cuisine.reinforcement_learning.obs_converter.base_converter_onehot.BaseStateConverterOnehot"
 log_path: "logs/reinforcement_learning"
 checkpoint_path: "rl_agent_checkpoints"
 render_mode: "rgb_array"

cooperative_cuisine/reinforcement_learning/config/model/A2C.yaml

@@ -4,6 +4,9 @@ model_name: "A2C"
 model_type:
   _partial_: true
   _target_: stable_baselines3.A2C
+model_type_inference:
+  _partial_: true
+  _target_: stable_baselines3.A2C.load
 total_timesteps:  3_000_000 # hendric sagt eher so 300_000_000 schritte
 number_envs_parallel:  64
 learning_rate: 0.0007

cooperative_cuisine/reinforcement_learning/config/model/DQN.yaml

@@ -4,6 +4,9 @@ model_name: "DQN"
 model_type:
   _partial_: true
   _target_: stable_baselines3.DQN
+model_type_inference:
+  _partial_: true
+  _target_: stable_baselines3.DQN.load
 total_timesteps:  3_000_000 # hendric sagt eher so 300_000_000 schritte
 number_envs_parallel:  64
 learning_rate: 0.0001

cooperative_cuisine/reinforcement_learning/config/model/PPO.yaml

@@ -4,6 +4,9 @@ model_name: "PPO"
 model_type:
   _partial_: true
   _target_: stable_baselines3.PPO
+model_type_inference:
+  _partial_: true
+  _target_: stable_baselines3.PPO.load
 total_timesteps:  3_000_000 # hendric sagt eher so 300_000_000 schritte
 number_envs_parallel:  64
 learning_rate: 0.0003

cooperative_cuisine/reinforcement_learning/gym_env.py

-import importlib
 import json
 import random
 from abc import abstractmethod
@@ -11,6 +10,7 @@ import cv2
 import numpy as np
 import yaml
 from gymnasium import spaces, Env
+from hydra.utils import instantiate
 from omegaconf import OmegaConf
 
 from cooperative_cuisine import ROOT_DIR
@@ -20,7 +20,6 @@ from cooperative_cuisine.environment import (
     Environment,
 )
 from cooperative_cuisine.pygame_2d_vis.drawing import Visualizer
-from hydra.utils import instantiate
 
 
 class SimpleActionSpace(Enum):

cooperative_cuisine/reinforcement_learning/run_single_agent.py

@@ -6,22 +6,25 @@ from stable_baselines3 import DQN, A2C, PPO
 from gym_env import EnvGymWrapper
 import hydra
 from omegaconf import DictConfig, OmegaConf
-from hydra.utils import instantiate
+from hydra.utils import instantiate, call
+
 
 @hydra.main(version_base="1.3", config_path="config", config_name="rl_config")
 def main(cfg: DictConfig):
     additional_config = OmegaConf.to_container(cfg.additional_configs, resolve=True)
-    model_save_path = "logs/reinforcement_learning/rl_agent_checkpoints/overcooked_DQN.zip"
-    model_save_path = additional_config["log_path"] + "/" + additional_config["checkpoint_path"] +"/"+ additional_config[
-        "project_name"] + "_" + OmegaConf.to_container(cfg.model, resolve=True)["model_name"]
-    model_class = instantiate(cfg.model.model_type)
-    model = model_class.load(model_save_path)
+    model_save_path = additional_config["log_path"] + "/" + additional_config["checkpoint_path"] + "/" + \
+                      additional_config["project_name"] + "_" + OmegaConf.to_container(cfg.model, resolve=True)[
+                          "model_name"]
+    model_class = call(cfg.model.model_type_inference)
+    model = model_class(model_save_path)
     env = EnvGymWrapper(cfg)
 
-    # check_env(env)
+    #check_env(env)
     obs, info = env.reset()
+    print(obs)
     while True:
         action, _states = model.predict(obs, deterministic=False)
+        print(action)
         obs, reward, terminated, truncated, info = env.step(int(action))
         print(reward)
         rgb_img = env.render()

cooperative_cuisine/reinforcement_learning/train_single_agent.py

@@ -54,7 +54,7 @@ def main(cfg: DictConfig):
     model_save_name = additional_configs["project_name"] + "_" + OmegaConf.to_container(cfg.model, resolve=True)["model_name"]
     model_save_path = rl_agent_checkpoints / model_save_name
     filtered_config = {k: v for k, v in config.items() if
-                       k not in ["env_id", "policy_type", "model_name", "model_type", "total_timesteps", "number_envs_parallel"] and v != 'None'}
+                       k not in ["env_id", "policy_type", "model_name", "model_type", "model_type_inference" ,"total_timesteps", "number_envs_parallel"] and v != 'None'}
     model = model_class(
         env=env,
         **filtered_config