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video demo

Reproduce (performance of) the following reinforcement learning methods:


Install dependencies by pip install 'gym[atari]'.

With ALE (paper's setting):

Download an atari rom, e.g.:


Start Training:

./ --env breakout.bin
# use `--algo` to select other DQN algorithms. See `-h` for more options.

Watch the agent play:

# Download pretrained models or use one you trained:
./ --env breakout.bin --task play --load DoubleDQN-breakout.bin.npz

Evaluation of 50 episodes:

./ --env breakout.bin --task eval --load DoubleDQN-breakout.bin.npz

With gym's Atari:

Install gym and atari_py. Use --env BreakoutDeterministic-v4 instead of the ROM file.


Claimed performance in the paper can be reproduced, on several games I've tested with.


Environment Avg Score Download
breakout.bin 465 ⬇️
seaquest.bin 8686 ⬇️
ms_pacman.bin 3323 ⬇️
beam_rider.bin 15835 ⬇️


On one GTX 1080Ti, the ALE version took ~2 hours of training to reach 21 (maximum) score on Pong, ~10 hours of training to reach 400 score on Breakout. It runs at 100 batches (6.4k trained frames, 400 seen frames, 1.6k game frames) per second on GTX 1080Ti. This is likely the fastest open source TF implementation of DQN.

A3C code and models for Atari games in OpenAI Gym are released in examples/A3C-Gym

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