55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
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from absl import logging, flags, app
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from environment.GoEnv import Go
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import time, os
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import numpy as np
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from agent.agent import RandomAgent
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import tensorflow as tf
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FLAGS = flags.FLAGS
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flags.DEFINE_integer("num_train_episodes", 10,
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"Number of training episodes for each base policy.")
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flags.DEFINE_integer("num_eval", 10,
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"Number of evaluation episodes")
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flags.DEFINE_integer("eval_every", 2000,
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"Episode frequency at which the agents are evaluated.")
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flags.DEFINE_integer("learn_every", 128,
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"Episode frequency at which the agents are evaluated.")
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flags.DEFINE_list("hidden_layers_sizes", [
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128
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], "Number of hidden units in the avg-net and Q-net.")
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flags.DEFINE_integer("replay_buffer_capacity", int(2e5),
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"Size of the replay buffer.")
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flags.DEFINE_integer("reservoir_buffer_capacity", int(2e6),
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"Size of the reservoir buffer.")
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def main(unused_argv):
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begin = time.time()
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env = Go()
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agents = [RandomAgent(idx) for idx in range(2)]
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for ep in range(FLAGS.num_eval):
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time_step = env.reset()
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while not time_step.last():
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player_id = time_step.observations["current_player"]
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if player_id == 0:
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agent_output = agents[player_id].step(time_step)
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else:
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agent_output = agents[player_id].step(time_step)
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action_list = agent_output.action
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time_step = env.step(action_list)
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print(time_step.observations["info_state"][0])
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# Episode is over, step all agents with final info state.
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# for agent in agents:
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agents[0].step(time_step)
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agents[1].step(time_step)
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print(time_step.rewards, env.get_current_board())
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print('Time elapsed:', time.time()-begin)
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if __name__ == '__main__':
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app.run(main)
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