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This repo contains python implementation to the cliff walking problem from RL Introduction by Sutton & Barto Example 6.6.

Python 100.00%

expected-sarsa q-learning reinforcement-learning temporal-differencing-learning

reinforcement-learning-cliff-walking's Introduction

Reinforcement-Learning-Cliff-Walking

This repo contains python implementation to the cliff walking problem from RL Introduction by Sutton & Barto Example 6.6.

The purpose is to implement TD(0) policy evaluation and also Q-Learning, Expected Sarsa for policy control.

Table of Contents

Rules
RL algorithms
Setup

Rules

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 Cli↵.”
Stepping into this region incurs a reward of 100 and sends the agent instantly back to the start.

RL algorithms

TD Zero

Q Learning

Expected Sarsa

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