mirror of
https://github.com/illiumst/marl-factory-grid.git
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85 lines
3.6 KiB
Python
85 lines
3.6 KiB
Python
from marl_factory_grid.algorithms.rl.RL_runner import rerun_dirt_quadrant_agent1_training, \
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rerun_two_rooms_agent1_training, rerun_two_rooms_agent2_training, dirt_quadrant_multi_agent_rl_eval, \
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two_rooms_multi_agent_rl_eval
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from marl_factory_grid.algorithms.tsp.TSP_runner import dirt_quadrant_multi_agent_tsp_eval, \
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two_rooms_multi_agent_tsp_eval
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###### Coin-quadrant environment ######
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def coin_quadrant_single_agent_training():
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""" Rerun training of RL-agent in coins_quadrant (dirt_quadrant) environment.
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The trained model and additional training metrics are saved in the study_out folder. """
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rerun_dirt_quadrant_agent1_training()
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def coin_quadrant_RL_multi_agent_eval_emergent():
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""" Rerun multi-agent evaluation of RL-agents in coins_quadrant (dirt_quadrant)
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environment, with occurring emergent phenomenon. Evaluation takes trained models
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from study_out/run0 for both agents."""
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dirt_quadrant_multi_agent_rl_eval(emergent_phenomenon=True)
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def coin_quadrant_RL_multi_agent_eval_prevented():
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""" Rerun multi-agent evaluation of RL-agents in coins_quadrant (dirt_quadrant)
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environment, with emergence prevention mechanism. Evaluation takes trained models
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from study_out/run0 for both agents."""
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dirt_quadrant_multi_agent_rl_eval(emergent_phenomenon=False)
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def coin_quadrant_TSP_multi_agent_eval_emergent():
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""" Rerun multi-agent evaluation of TSP-agents in coins_quadrant (dirt_quadrant)
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environment, with occurring emergent phenomenon. """
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dirt_quadrant_multi_agent_tsp_eval(emergent_phenomenon=True)
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def coin_quadrant_TSP_multi_agent_eval_prevented():
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""" Rerun multi-agent evaluation of TSP-agents in coins_quadrant (dirt_quadrant)
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environment, with emergence prevention mechanism. """
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dirt_quadrant_multi_agent_tsp_eval(emergent_phenomenon=False)
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###### Two-rooms environment ######
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def two_rooms_agent1_training():
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""" Rerun training of left RL-agent in two_rooms environment.
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The trained model and additional training metrics are saved in the study_out folder. """
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rerun_two_rooms_agent1_training()
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def two_rooms_agent2_training():
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""" Rerun training of right RL-agent in two_rooms environment.
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The trained model and additional training metrics are saved in the study_out folder. """
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rerun_two_rooms_agent2_training()
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def two_rooms_RL_multi_agent_eval_emergent():
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""" Rerun multi-agent evaluation of RL-agents in two_rooms environment, with
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occurring emergent phenomenon. Evaluation takes trained models
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from study_out/run1 for agent1 and study_out/run2 for agent2. """
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two_rooms_multi_agent_rl_eval(emergent_phenomenon=True)
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def two_rooms_RL_multi_agent_eval_prevented():
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""" Rerun multi-agent evaluation of RL-agents in two_rooms environment, with
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emergence prevention mechanism. Evaluation takes trained models
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from study_out/run1 for agent1 and study_out/run2 for agent2. """
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two_rooms_multi_agent_rl_eval(emergent_phenomenon=False)
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def two_rooms_TSP_multi_agent_eval_emergent():
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""" Rerun multi-agent evaluation of TSP-agents in two_rooms environment, with
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occurring emergent phenomenon. """
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two_rooms_multi_agent_tsp_eval(emergent_phenomenon=True)
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def two_rooms_TSP_multi_agent_eval_prevented():
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""" Rerun multi-agent evaluation of TSP-agents in two_rooms environment, with
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emergence prevention mechanism. """
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two_rooms_multi_agent_tsp_eval(emergent_phenomenon=False)
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if __name__ == '__main__':
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# Select any of the above functions to rerun the respective part
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# from our evaluation section of the paper
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coin_quadrant_RL_multi_agent_eval_prevented()
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