8c2aea2add
* exported rl-algs * more stuff from rl-algs * run slow tests * re-exported rl_algs * re-exported rl_algs - fixed problems with serialization test and test_cartpole * replaced atari_arg_parser with common_arg_parser * run.py can run algos from both baselines and rl_algs * added approximate humanoid reward with ppo2 into the README for reference * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * very dummy commit to RUN BENCHMARKS * serialize variables as a dict, not as a list * running_mean_std uses tensorflow variables * fixed import in vec_normalize * dummy commit to RUN BENCHMARKS * dummy commit to RUN BENCHMARKS * flake8 complaints * save all variables to make sure we save the vec_normalize normalization * benchmarks on ppo2 only RUN BENCHMARKS * make_atari_env compatible with mpi * run ppo_mpi benchmarks only RUN BENCHMARKS * hardcode names of retro environments * add defaults * changed default ppo2 lr schedule to linear RUN BENCHMARKS * non-tf normalization benchmark RUN BENCHMARKS * use ncpu=1 for mujoco sessions - gives a bit of a performance speedup * reverted running_mean_std to user property decorators for mean, var, count * reverted VecNormalize to use RunningMeanStd (no tf) * reverted VecNormalize to use RunningMeanStd (no tf) * profiling wip * use VecNormalize with regular RunningMeanStd * added acer runner (missing import) * flake8 complaints * added a note in README about TfRunningMeanStd and serialization of VecNormalize * dummy commit to RUN BENCHMARKS * merged benchmarks branch
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import argparse
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from baselines import deepq
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from baselines.common import set_global_seeds
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from baselines import bench
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from baselines import logger
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from baselines.common import retro_wrappers
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import retro
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def main():
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--env', help='environment ID', default='SuperMarioBros-Nes')
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parser.add_argument('--gamestate', help='game state to load', default='Level1-1')
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parser.add_argument('--seed', help='seed', type=int, default=0)
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parser.add_argument('--num-timesteps', type=int, default=int(10e6))
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args = parser.parse_args()
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logger.configure()
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set_global_seeds(args.seed)
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env = retro_wrappers.make_retro(game=args.env, state=args.gamestate, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE)
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env.seed(args.seed)
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env = bench.Monitor(env, logger.get_dir())
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env = retro_wrappers.wrap_deepmind_retro(env)
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model = deepq.models.cnn_to_mlp(
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convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
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hiddens=[256],
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dueling=True
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)
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act = deepq.learn(
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env,
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q_func=model,
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lr=1e-4,
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max_timesteps=args.num_timesteps,
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buffer_size=10000,
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exploration_fraction=0.1,
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exploration_final_eps=0.01,
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train_freq=4,
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learning_starts=10000,
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target_network_update_freq=1000,
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gamma=0.99,
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prioritized_replay=True
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)
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act.save()
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env.close()
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if __name__ == '__main__':
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main()
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