NickKaparinos/OpenAI-Gym-Projects

OpenAI Gym environment solutions using Deep Reinforcement Learning.

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This project offers pre-built solutions for various Gym environments, a standard toolkit for developing and comparing reinforcement learning algorithms. It provides trained agents and their learning curves for classic control problems like CartPole and MountainCar, Box2D simulations such as LunarLander, and even robotics and Atari game environments. It's intended for reinforcement learning researchers and practitioners who want to understand, benchmark, or compare different deep reinforcement learning algorithms on well-known control tasks.

No commits in the last 6 months.

Use this if you are a reinforcement learning practitioner looking for working examples and solutions to common Gym environments using established deep RL algorithms to learn from or compare against.

Not ideal if you are looking for a general-purpose library to build new reinforcement learning environments from scratch or if your problem domain is outside of the classic control and simulation tasks covered by Gym.

reinforcement-learning control-systems robotics-simulation game-AI algorithm-benchmarking
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 14 / 25

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80

Forks

11

Language

Python

License

MIT

Last pushed

May 25, 2022

Commits (30d)

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