WEwesturner·Sep 12, 2023
The latest on winning Atari with RL+ FWIU:
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#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 conditio
Read the rest
n 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#...
MuZero: https://en.wikipedia.org/wiki/MuZero
MuZero-unplugged with PyTorch:
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
Farama-Foundatiom/MO-Gymnasiun:
"Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium