Setting up the Cliff Walking Environment for Reinforcement Learning (RL)

Summary

The OpenAI Gym's Cliff Walking environment is a reinforcement learning challenge where an agent must navigate a grid to reach a goal without falling off a cliff, learning through rewards and penalties.

Abstract

The Cliff Walking environment in OpenAI Gym is a classic problem in reinforcement learning, where an agent must find the optimal path in a grid world from the bottom-left corner to the bottom-right corner, avoiding cliffs that lead to a significant penalty. The agent receives a negative reward for each step and must learn to avoid the cliff cells to maximize its total reward. The setup involves using Python with the OpenAI Gym library to create the environment, reset it, and then iterate through a number of steps, rendering the environment, taking actions, and processing the resulting observations, rewards, and done flags. The agent's goal is to learn the shortest path to the goal state through trial and error, guided by the reward structure that penalizes falling off the cliff or taking longer routes.

Opinions

The Cliff Walking environment is considered a toy text environment within Gym, implying it is a simplified scenario for learning and testing reinforcement learning algorithms.
The environment's design, with its clear reward and penalty system, is intended to encourage the development of algorithms that can efficiently learn optimal policies.
The task is intuitive for humans, but it presents a non-trivial learning challenge for RL agents, highlighting the complexity of learning through interaction in seemingly simple environments.
The use of a negative reward for each step encourages the agent to find the shortest path to the goal, emphasizing efficiency in the learning process.
The significant penalty for falling off the cliff is a strong deterrent, teaching the agent to avoid dangerous actions that lead to catastrophic outcomes.

Setting up the Cliff Walking Environment for Reinforcement Learning (RL)

The OpenAI Gym’s Cliff Walking environment is a classic reinforcement learning task in which an agent must navigate a grid world to reach a goal state while avoiding falling off of a cliff. The Cliff Walking environment is another environment within the toy text environments in Gym.

Gym’s Cliff Walking environment

The agent starts at the bottom-left corner of the grid and must reach the bottom-right corner. The grid is composed of safe cells, which the agent can move through freely, and cliff cells, which the agent must avoid.

The agent can move in four directions: up, down, left, and right. If the agent falls off a cliff, it will be returned to the starting position and incur a penalty of -100; otherwise, the agent receives a reward of -1 for each step it takes while not having completed the episode. The state or observation is taken as the current position in the grid world. The goal is to find the optimal policy that maximizes the total reward, which amounts to finding the shortest path in this environment.

Here is how to setup the Cliff Walking environment using Python and the OpenAI Gym library:

import gym

# Create the Cliff Walking environment
env = gym.make('CliffWalking-v0')

# Reset the environment to its initial state
observation = env.reset()

# Set the number of steps to take
num_steps = 10

# Take the given number of steps
for i in range(num_steps):
    # Render the environment to the screen
    env.render()

    # Choose a random action
    action = env.action_space.sample()

    # Take the action and get the next observation, reward, and done flag
    observation, reward, done, info = env.step(action)

    # Print some environmental values
    print(f'Step {i}: observation={observation}, \
          reward={reward}, done={done}, info={info}')

    # If the episode is over, reset the environment
    if done:
        observation = env.reset()

# Close the environment
env.close()

Sample Output:

The x represents the agent’s current location during the epsiode. The o’s represent safe cells, and the C’s represent the dangerous cliff cells. The RL agent’s task is to learn the optimal path through this environment, which should be intuitively obvious upon inspection. However, the RL agent needs to learn this from the reward structure, i.e., that it shouldn’t fall of the cliff or take some needlessly long path in the environment before stumbling into the goal state.