This repository contains the Gymnasium environments of EduGym: a suite for reinforcement learning education.
See our website for more information and see the code in the Notebooks to illustrate the specific challenge and possible solution approaches that the environments are supposed to teach.
We provide both Environments (edugym/envs) and Agents (edugym/agents) to train on.
Each can be executed as a main program.
Executing an agent will train and evaluate it producing a learning curve, e.g.:
python3 -m edugym.agents.QLearningAgentExecuting an environment lets the user play an episode themselves. The key mapping will be output in the terminal:
python3 -m edugym.envs.supermarketBelow is a table of the available Agents / Environments paths one can execute
| Environments | Agents |
|---|---|
| edugym.envs.boulder | edugym.agents.DynaAgent |
| edugym.envs.catch | edugym.agents.DynamicProgrammingAgent |
| edugym.envs.golf | edugym.agents.ModelLearningAgent |
| edugym.envs.memorycorridor | edugym.agents.PrioritizedSweepingAgent |
| edugym.envs.roadrunner | edugym.agents.QLearningAgent |
| edugym.envs.study | edugym.agents.QLearningAgentDiscretized |
| edugym.envs.supermarket | edugym.agents.QLearningAgentFrameStacking |
| edugym.envs.tamagotchi | edugym.agents.SarsaAgent |
| edugym.envs.trashbot |