# Nasdin/ReinforcementLearning-AtariGame

<https://repo.masters-masters.com/en/repos/Nasdin/ReinforcementLearning-AtariGame>

[Home](https://repo.masters-masters.com/en) › [Nasdin](https://repo.masters-masters.com/en/owners/Nasdin) › [ReinforcementLearning-AtariGame](https://repo.masters-masters.com/en/repos/Nasdin/ReinforcementLearning-AtariGame)

Nasdin/ReinforcementLearning-AtariGame has 196 stars on GitHub. Its RepoMasters card: Prototype tier. Pytorch LSTM RNN for reinforcement learning to play…

| Field | Value |
| --- | --- |
| Owner | [Nasdin](https://repo.masters-masters.com/en/owners/Nasdin) |
| Stars | 196 |
| Forks | 38 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | BSD-3-Clause |
| Created | 2017 |
| Topics | a3c, a3c-lstm, pytorch, openai-gym, reinforcement-learning, universe, lstm, adam, rmsprop, reinforcement-learning-algorithms |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [Nasdin/ReinforcementLearning-AtariGame](https://github.com/Nasdin/ReinforcementLearning-AtariGame) |

## Repository

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.