Atari2600 Training / Evaluation with RLlib

Jinwoo Park (Curt)

Last update: Dec 12, 2021

Related tags

Game Development atari2600-rllib

Overview

Training Atari2600 by Reinforcement Learning

Train Atari2600 and check how it works!

How to Setup

You can setup packages on your local env.

$ make setup

or you can run the docker image.

$ make docker-run

How to Run

$ python run_atari.py --help

usage: run_atari.py [-h] [--env ENV] [--checkpoint CHECKPOINT] [--n-iters N_ITERS] [--n-workers N_WORKERS] [--gpu]

optional arguments:
  -h, --help            show this help message and exit
  --env ENV             Atari-2600 env name (Default: Breakout-v0)
  --n-iters N_ITERS     Training iteration number (Default: 10)
  --n-workers N_WORKERS
                        Number of workers for sampling (Default: 4)
  --checkpoint CHECKPOINT
                        Checkpoint path for inference
  --gpu                 Use GPU (Default: False)
  --render              Render env during eval

More Atari2600 environments can be found at: https://gym.openai.com/envs/#atari

For Training

$ python run_atari.py --gpu  # GPU
$ python run_atari.py  # CPU

For Evaluation

$ python run_atari.py --render --checkpoint path-to-checkpoint

For Developers

For clean code, you can run formatting or linting.

$ make format  # formatting
$ make lint  # linting

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Atari2600 Training / Evaluation with RLlib

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Overview

Training Atari2600 by Reinforcement Learning

How to Setup

How to Run

For Training

For Evaluation

For Developers

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Atari2600 Training / Evaluation with RLlib

Related tags

Overview

Training Atari2600 by Reinforcement Learning

How to Setup

How to Run

For Training

For Evaluation

For Developers

You might also like...

YoloV5 implemented by TensorFlow2 , with support for training, evaluation and inference.

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.

​TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

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