https://github.com/amir22010/ppo-dash
PPO Dash: Improving Generalization in Deep Reinforcement Learning
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (5.4%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
PPO Dash: Improving Generalization in Deep Reinforcement Learning
Basic Info
- Host: GitHub
- Owner: Amir22010
- Language: Python
- Default Branch: master
- Size: 315 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of Sohojoe/ppo-dash
Created almost 7 years ago
· Last pushed almost 7 years ago
https://github.com/Amir22010/ppo-dash/blob/master/
# PPO-Dash Code for reproducing the results found in [PPO Dash: Improving Generalization in Deep Reinforcement Learning](https://arxiv.org/abs/1907.06704) # About PPO-Dash is a modified version of the PPO algrothem that utalises the following optimizations and best practices: * Action Space Reduction * Frame Stack Reduction * Large Scale Hyperparameters * Vector Observations * Normalized Observations * Reward Hacking * Recurrent Memory PPO-Dash was able to solve the first 10 levels of the Obsticle Tower Enviroment without the need for demonstrations or curosity based algorthemic enhancements. The version of PPO-Dash in the technical paper, [placed 2nd](https://blogs.unity3d.com/2019/05/15/obstacle-tower-challenge-round-2-begins-today/) in Round One of the [Obsticle Tower Challenge](https://www.aicrowd.com/challenges/unity-obstacle-tower-challenge) with an average score of 10. We were able to reproduce this score in Round Two of the challenge, with a minor modifiaction (randomizing the themes during in training). We [placed 4th overall](https://www.aicrowd.com/challenges/unity-obstacle-tower-challenge/leaderboards), with a score of 10.8 with the addition of demonstrations. # Reproducing Results To reproduce the results listed in the paper and for round one of the competition, see [ReproduceRound1](ReproduceRound1.md) # Acknowlegements This codebase derives from [pytorch-a2c-ppo-acktr](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail) - [#8258f95](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/commit/8258f95d6c1959d02c6a412415138b95c32837a0) # Citation If you use PPO-Dash in your research, we ask that you cite the [technical report](https://arxiv.org/abs/1907.06704) as a reference.
Owner
- Name: Amir Khan
- Login: Amir22010
- Kind: user
- Location: India
- Repositories: 3
- Profile: https://github.com/Amir22010
working on developing a state of art AI solutions mainly in computer vision, chat bots and nlp domain. building an awesome AI as a professional developer 😍.