Based on PARL, the A2C algorithm of deep reinforcement learning has been reproduced, reaching the same level of indicators as the paper in Atari benchmarks.
Please see here to know more about Atari games.
Performance of A2C on some envrionments in training process after 10 million sample steps.
- paddlepaddle>=2.0.0
- parl>=1.4.3
- gym==0.12.1
- atari-py==0.1.7
- opencv-python
At first, we can start a local cluster with 5 CPUs:
xparl start --port 8110 --cpu_num 5Note 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