An OpenAI Gym environment for Cliff Walking problem (from Sutton and Barto book).
This environment is presented in the Sutton and Barto's book: Reinforcement Learning An Introduction (2 ed., 2018). The text and image below are from the book.
Example 6.6: Cliff Walking This gridworld example compares Sarsa and Q-learning, highlighting the difference between on-policy (Sarsa) and off-policy (Q-learning) methods. Consider the gridworld shown below. This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down,right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff”. Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start.
This minimalist package was created to be used as a OpenAI Gym environment. The code was based on the exercises of Reinforcement Learning Specialization from Coursera.
There is not a renderization of the environment (issue #1). Pull requests with this contribution are welcome. :)
To install the package you need to clone (or download) the repository and use the command pip install -e gym-cliffwalking.
To create an instance of the environment in python code use gym.make('gym_cliffwalking:cliffwalking-v0').