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All relevant functional code for A2C Dirt Quadrant setting with small changes to the environment + Different configs for single agent and multiagent settings

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Julian Schönberger
2024-05-06 12:33:37 +02:00
parent 55026eda12
commit 3c54d04f9f
13 changed files with 652 additions and 174 deletions
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27
studies/marl_adapted.py
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@ -3,17 +3,36 @@ from pathlib import Path
from marl_factory_grid.algorithms.marl.a2c_dirt import A2C
from marl_factory_grid.algorithms.utils import load_yaml_file
if __name__ == '__main__':
def dirt_quadrant_single_agent_training():
cfg_path = Path('../marl_factory_grid/algorithms/marl/configs/dirt_quadrant_config.yaml')
train_cfg = load_yaml_file(cfg_path)
# Use environment config with fixed spawnpoints for eval
eval_cfg = copy.deepcopy(train_cfg)
eval_cfg["env"]["env_name"] = "custom/dirt_quadrant" # Options: two_rooms_one_door_modified, dirt_quadrant
eval_cfg["env"]["env_name"] = "custom/dirt_quadrant_eval_config"
print("Training phase")
agent = A2C(train_cfg, eval_cfg)
agent.train_loop()
agent.plot_reward_development()
print("Evaluation phase")
agent.eval_loop(10)
# Have consecutive episode for eval in single agent case
train_cfg["algorithm"]["pile_all_done"] = "all"
# agent.load_agents(["run0", "run1"])
agent.eval_loop(10)
def dirt_quadrant_multi_agent_eval():
cfg_path = Path('../marl_factory_grid/algorithms/marl/configs/MultiAgentConfigs/dirt_quadrant_config.yaml')
train_cfg = load_yaml_file(cfg_path)
# Use environment config with fixed spawnpoints for eval
eval_cfg = copy.deepcopy(train_cfg)
eval_cfg["env"]["env_name"] = "custom/MultiAgentConfigs/dirt_quadrant_eval_config"
agent = A2C(train_cfg, eval_cfg)
print("Evaluation phase")
agent.load_agents(["run0", "run1"])
agent.eval_loop(10)
if __name__ == '__main__':
dirt_quadrant_single_agent_training()