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  5. Reinforcement Learning with Gymnasium in Python

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Exercise

Exploring state and action spaces

The Cliff Walking environment involves an agent crossing a grid world from start to goal while avoiding falling off a cliff. If the player moves to a cliff location it returns to the start location. The player makes moves until they reach the goal, which ends the episode. Your task is to explore the state and action spaces of this environment.

Image showing an animation for the cliff walking environment.

Instructions

100 XP
  • Create an environment instance for Cliff Walking with with the environment ID CliffWalking.
  • Compute the size of the action space and store it in num_actions.
  • Compute the size of the state space and store it in num_states.