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Bo Zhou a7bf01a708 | 2 years ago | |
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.benchmark | 2 years ago | |
README.md | 2 years ago | |
actor.py | 2 years ago | |
atari_agent.py | 2 years ago | |
atari_model.py | 2 years ago | |
impala_config.py | 2 years ago | |
train.py | 2 years ago |
Based on PARL, the IMPALA algorithm of deep reinforcement learning is reproduced, and the same level of indicators of the paper is reproduced in the classic Atari game.
Paper: IMPALA in Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Please see here to know more about Atari games.
Result with one learner (in a P40 GPU) and 32 actors (in 32 CPUs).
PongNoFrameskip-v4: mean_episode_rewards can reach 18-19 score in about 7~10 minutes.
Results of other games in an hour.
At first, We can start a local cluster with 32 CPUs:
xparl start --port 8010 --cpu_num 32
Note that if you have started a master before, you don't have to run the above
command. For more information about the cluster, please refer to our
documentation
Then we can start the distributed training by running:
python train.py
PARL 是一个高性能、灵活的强化学习框架
Python C++ JavaScript Shell Markdown other
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