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https://github.com/illiumst/marl-factory-grid.git
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defbaf6f93
# Conflicts: # marl_factory_grid/algorithms/static/TSP_base_agent.py # marl_factory_grid/utils/plotting/plot_single_runs.py # marl_factory_grid/utils/renderer.py # test_run.py
96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
import copy
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from pathlib import Path
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from marl_factory_grid.algorithms.marl.a2c_dirt import A2C
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from marl_factory_grid.algorithms.utils import load_yaml_file
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def single_agent_training(config_name):
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cfg_path = Path(f'../marl_factory_grid/algorithms/marl/configs/{config_name}_config.yaml')
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train_cfg = load_yaml_file(cfg_path)
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# Use environment config with fixed spawnpoints for eval
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eval_cfg = copy.deepcopy(train_cfg)
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eval_cfg["env"]["env_name"] = f"custom/{config_name}_eval_config"
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print("Training phase")
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agent = A2C(train_cfg, eval_cfg)
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agent.train_loop()
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print("Evaluation phase")
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# Have consecutive episode for eval in single agent case
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train_cfg["algorithm"]["pile_all_done"] = "all"
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agent.eval_loop(10)
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def single_agent_eval(config_name, run):
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cfg_path = Path(f'../marl_factory_grid/algorithms/marl/configs/{config_name}_config.yaml')
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train_cfg = load_yaml_file(cfg_path)
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# Use environment config with fixed spawnpoints for eval
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eval_cfg = copy.deepcopy(train_cfg)
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eval_cfg["env"]["env_name"] = f"custom/{config_name}_eval_config"
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agent = A2C(train_cfg, eval_cfg)
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print("Evaluation phase")
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agent.load_agents(run)
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agent.eval_loop(1)
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def multi_agent_eval(config_name, runs, emergent_phenomenon=False):
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cfg_path = Path(f'../marl_factory_grid/algorithms/marl/configs/MultiAgentConfigs/{config_name}_config.yaml')
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train_cfg = load_yaml_file(cfg_path)
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# Use environment config with fixed spawnpoints for eval
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eval_cfg = copy.deepcopy(train_cfg)
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eval_cfg["env"]["env_name"] = f"custom/MultiAgentConfigs/{config_name}_eval_config"
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# Sanity setting of required attributes and configs
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if config_name == "two_rooms_one_door_modified":
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if emergent_phenomenon:
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eval_cfg["env"]["env_name"] = f"custom/MultiAgentConfigs/{config_name}_eval_config_emergent"
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eval_cfg["algorithm"]["auxiliary_piles"] = False
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else:
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eval_cfg["algorithm"]["auxiliary_piles"] = True
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elif config_name == "dirt_quadrant":
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if emergent_phenomenon:
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eval_cfg["algorithm"]["pile-order"] = "dynamic"
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else:
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eval_cfg["algorithm"]["pile-order"] = "smart"
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agent = A2C(train_cfg, eval_cfg)
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print("Evaluation phase")
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agent.load_agents(runs)
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agent.eval_loop(1)
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def dirt_quadrant_single_agent_training():
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single_agent_training("dirt_quadrant")
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def two_rooms_one_door_modified_single_agent_training():
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single_agent_training("two_rooms_one_door_modified")
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def dirt_quadrant_single_agent_eval(agent_name):
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if agent_name == "Sigmund":
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run = "run0"
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elif agent_name == "Wolfgang":
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run = "run1"
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single_agent_eval("dirt_quadrant", [run])
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def two_rooms_one_door_modified_single_agent_eval(agent_name):
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if agent_name == "Sigmund":
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run = "run2"
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elif agent_name == "Wolfgang":
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run = "run3"
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single_agent_eval("two_rooms_one_door_modified", [run])
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def dirt_quadrant_5_multi_agent_eval(emergent_phenomenon):
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multi_agent_eval("dirt_quadrant", ["run4", "run5"], emergent_phenomenon)
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def dirt_quadrant_5_multi_agent_ctde_eval(emergent_phenomenon): # run7 == run4
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multi_agent_eval("dirt_quadrant", ["run4", "run7"], emergent_phenomenon)
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def two_rooms_one_door_modified_multi_agent_eval(emergent_phenomenon):
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multi_agent_eval("two_rooms_one_door_modified", ["run2", "run3"], emergent_phenomenon)
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if __name__ == '__main__':
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dirt_quadrant_5_multi_agent_ctde_eval(True) |