PARL/examples/A2C

• Reproduce A2C with PARL
• • Atari games introduction
  • Benchmark result
• How to use
• • Dependencies
  • Distributed Training
  • • Learner
    • Actors (Suggest: 5 actors in 5 CPUs)
  • Reference

Reproduce A2C with PARL

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.

A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C). Instead of updating asynchronously in A3C or GA3C, A2C uses a synchronous approach that waits for each actor to finish its sampling before performing an update. Since loss definition of these A3C variants are identical, we use a common a3c algotrithm parl.algorithms.A3C for A2C and GA3C examples.

Atari games introduction

Please see here to know more about Atari games.

Benchmark result

Mean episode reward in training process after 10 million sample steps.

Alien (1278)	Amidar (380)	Assault (4659)	Aterix (3883)	Atlantis (3040000)
Pong (20)	Breakout (405)	Beamrider (3394)	Qbert (14528)	SpaceInvaders (819)

How to use

Dependencies

• paddlepaddle>=1.5.1
• parl
• gym
• atari-py

Distributed Training

Learner

python train.py 

Actors (Suggest: 5 actors in 5 CPUs)

for i in $(seq 1 5); do
    python actor.py &
done;
wait

You can change training settings (e.g. env_name, server_ip) in a2c_config.py.
Training result will be saved in log_dir/train/result.csv.

Reference

• Ray
• OpenAI Baselines: ACKTR & A2C