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| import sys | |
| import itertools | |
| if __name__ == '__main__': | |
| ''' | |
| Generate scripts to perform grid search on agents's hyperparameters. | |
| Defines the values to test for each hyperparameter. | |
| ''' | |
| tuning_dict = { | |
| # "PPO": { | |
| # "frames-per-proc": [20, 40, 80], | |
| # "lr": [1e-4, 1e-3], | |
| # "entropy-coef": [0, 0.01, 0.05], | |
| # "recurrence": [5, 10], | |
| # "epochs": [4, 8, 12], | |
| # "batch-size": [640, 1280, 2560], | |
| # "env": ["MiniGrid-CoinThief-8x8-v0 --env_args few_actions True", "MiniGrid-TalkItOutPolite-8x8-v0"] | |
| # }, | |
| "PPO-RND": { | |
| # rnd and ride | |
| "optim-eps": [1e-5, 1e-7], | |
| "entropy-coef": [0.01, 0.0001, 0.0005], | |
| "intrinsic-reward-learning-rate": [0.0001, 0.0004, 0.001], | |
| # "intrinsic-reward-coef": [0.1], | |
| # "intrinisc-reward-momentum": [0], | |
| "intrinsic-reward-epsilon": [0.01, 0.001, 0.0001], | |
| # "intrinsic-reward-alpha": [0.99], | |
| "intrinsic-reward-max-grad-norm": [1000, 40, 20, 1], | |
| # rnd | |
| # "intrinsic-reward-loss-coef": [0.1], | |
| "env": ["MiniGrid-CoinThief-8x8-v0 --env_args few_actions True", "MiniGrid-TalkItOutPolite-8x8-v0"] | |
| } | |
| } | |
| with open("hp_tuning_agent.txt", 'w') as f: | |
| for agent in tuning_dict: | |
| f.write('## {}\n'.format(agent)) | |
| current_agent_parameters = list(tuning_dict[agent].keys()) | |
| current_agent_hyperparams = tuning_dict[agent].values() | |
| for point in itertools.product(*current_agent_hyperparams): | |
| current_arguments = '' | |
| for i in range(len(current_agent_parameters)): | |
| current_arguments += ' --*' + current_agent_parameters[i] | |
| current_arguments += ' ' + str(point[i]) if point[i] is not None else '' | |
| if agent == "PPO-RND": | |
| f.write( | |
| '--slurm_conf jz_short_2gpus_32g --nb_seeds 8 --model PPO_RND_tuning --algo ppo -cs --frames 10000000 ' | |
| '--save-interval 100 --log-interval 100 ' | |
| '--dialogue --multi-modal-babyai11-agent ' | |
| '--exploration-bonus --exploration-bonus-type rnd --clipped-rewards ' | |
| '--arch original_endpool_res {}\n'.format(current_arguments)) | |
| else: | |
| f.write( | |
| '--slurm_conf jz_short_2gpus_32g --nb_seeds 8 --model PPO_RND_tuning --algo ppo -cs --frames 10000000 ' | |
| '--save-interval 100 --log-interval 100 ' | |
| '--dialogue --multi-modal-babyai11-agent ' | |
| '--arch original_endpool_res {}\n'.format(current_arguments)) | |