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Reinforcement Learning at UCLA IPAM RIPS 2018

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This project details a solution OpenAI's CarRacing-v0 game. This model uses a three layer DQN regularized with dropout and using a curriculum learning approach. The trained model exceeds 900 in average score, sufficient to meet the criterion for solution. More information at: https://github.com/openai/gym/wiki/Leaderboard#carracing-v0

# RL-2018
Reinforcement Learning at UCLA IPAM RIPS 2018. Industry Sponsor: Advanced Micro Devices.

Contributers: 
* Patrik Gerber*, University of Oxford
* Jiajing Guan*, George Mason University
* Elvis Nunez*, Johns Hopkins University
* Kaman Phamdo*, University of Maryland
* Tonmoy Monsoor, UCLA
* [Nicholas Malaya](https://nicholasmalaya.github.io), AMD Research

\* Denotes Equal Contribution.

Initial goals:
* Add papers and background
* Get OpenAI gym Sandbox working
* Get game working/imported

References:

https://blog.insightdatascience.com/reinforcement-learning-from-scratch-819b65f074d8

https://blog.openai.com/requests-for-research-2/

http://www.andreykurenkov.com/writing/ai/a-brief-history-of-game-ai/

https://blog.openai.com/retro-contest/

https://medium.com/@dhruvp/how-to-write-a-neural-network-to-play-pong-from-scratch-956b57d4f6e0

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