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An autonomous agent that learns to play Atari Bowling using Reinforcement Learning and Proximal Policy Optimization

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AlessandroPomponio/atari-bowling-ppo

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Playing Atari Bowling with Proximal Policy Optimization (PPO)

This notebook has been created by Alessandro Pomponio for the 2021 Autonomous and Adaptive System course, held by prof. Mirco Musolesi at the University of Bologna. It features an autonomous agent that learns to play the Atari Bowling game using Reinforcement Learning and Proximal Policy Optimization.

Repository content

The repository contains the Jupyter notebook used to train the agent (training time was ~36h on a quad-core VPS, cpu-only) and a report.

Examples of the Agent's behavior

Bowling.111.mp4
Bowling.strikes.mp4

Disclaimer

The code might contain errors, be wary of that!

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An autonomous agent that learns to play Atari Bowling using Reinforcement Learning and Proximal Policy Optimization

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