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Very old ML project from a master's student's thesis which is what originally got me into CS. He taught it to play NES games other than mario and had a good bre

Very old ML project from a master's student's thesis which is what originally got me into CS. He taught it to play NES games other than mario and had a good breakdown of his results. http://tom7.org/mario/ http://tom7.org/mario/

deleted 3y ago
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_joel 3y ago
Jeez, I remember when that came out, "very old", feels like yesterday
westurner 3y ago
The latest on winning Atari with RL+ FWIU: https://github.com/openai/retro https://github.com/openai/retro: > Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games. It uses various emulators that support the Libretro API, making it fairly easy to add new emulators. .nes is listed in the supported ROM types: https://retro.readthedocs.io/en/latest/integration.html#supported-rom-types https://retro.readthedocs.io/en/latest/integration.html#supp... > Integrating a Game: To integrate a game you need to define a done condition and a reward function. The done condition lets Gym Retro know when to end a game session, while the reward function provides a simple numeric goal for machine learning agents to maximize. > To define these, you find variables from the game’s memory, such as the player’s current score and lives remaining, and use those to create the done condition and reward function. An example done condition is when the `lives` variable is equal to 0, an example reward function is the change in the `score` variable. PPO Proximal Policy Optimization and OpenAI/baselines: https://retro.readthedocs.io/en/latest/getting_started.html#ppo https://retro.readthedocs.io/en/latest/getting_started.html#... MuZero: https://en.wikipedia.org/wiki/MuZero https://en.wikipedia.org/wiki/MuZero MuZero-unplugged with PyTorch: https://github.com/DHDev0/Muzero-unplugged https://github.com/DHDev0/Muzero-unplugged Farama-Foundation/Gymnasium is a fork of OpenAI/gym and it has support for additional Environments like MuJoCo: https://github.com/Farama-Foundation/Gymnasium#environments https://github.com/Farama-Foundation/Gymnasium#environments Farama-Foundatiom/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium https://github.com/Farama-Foundation/MO-Gymnasium

deleted 3y ago

[deleted]

_joel 3y ago

Jeez, I remember when that came out, "very old", feels like yesterday

westurner 3y ago

The latest on winning Atari with RL+ FWIU: https://github.com/openai/retro https://github.com/openai/retro: > Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games. It uses various emulators that support the Libretro API, making it fairly easy to add new emulators. .nes is listed in the supported ROM types: https://retro.readthedocs.io/en/latest/integration.html#supported-rom-types https://retro.readthedocs.io/en/latest/integration.html#supp... > Integrating a Game: To integrate a game you need to define a done condition and a reward function. The done condition lets Gym Retro know when to end a game session, while the reward function provides a simple numeric goal for machine learning agents to maximize. > To define these, you find variables from the game’s memory, such as the player’s current score and lives remaining, and use those to create the done condition and reward function. An example done condition is when the `lives` variable is equal to 0, an example reward function is the change in the `score` variable. PPO Proximal Policy Optimization and OpenAI/baselines: https://retro.readthedocs.io/en/latest/getting_started.html#ppo https://retro.readthedocs.io/en/latest/getting_started.html#... MuZero: https://en.wikipedia.org/wiki/MuZero https://en.wikipedia.org/wiki/MuZero MuZero-unplugged with PyTorch: https://github.com/DHDev0/Muzero-unplugged https://github.com/DHDev0/Muzero-unplugged Farama-Foundation/Gymnasium is a fork of OpenAI/gym and it has support for additional Environments like MuJoCo: https://github.com/Farama-Foundation/Gymnasium#environments https://github.com/Farama-Foundation/Gymnasium#environments Farama-Foundatiom/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium https://github.com/Farama-Foundation/MO-Gymnasium