Implementation of various reinforcement learning algorithms in examples obtained from the book "Reinforcement Learning: An Introduction, by Sutton and Barto".
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Updated
Feb 7, 2022 - MATLAB
Implementation of various reinforcement learning algorithms in examples obtained from the book "Reinforcement Learning: An Introduction, by Sutton and Barto".
This repo implements Deep Q-Network (DQN) for solving the Cliff Walking v0 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with the finest tuning.
Four reinforcement learning algorithms — Value Iteration, Direct Estimation, Q-Learning and REINFORCE — implemented, swept and compared on the Cliff Walking environment, with the learned policies visualized
Comparing SARSA, Q-Learning, and Double Q-Learning on Cliff Walking and Taxi-V3 Tasks
Implementation of Q-learning and SARSA algorithms in the Cliff Walking environment. Explore and compare reinforcement learning techniques.
Q-Learning agent that learns to navigate a 4×12 cliff-edge grid using ε-greedy exploration and Bellman updates — trained from scratch with NumPy + Gymnasium in 63 lines of Python.
🧗Comparative Reinforcement Learning analysis implementing SARSA (On-Policy) vs Q-Learning (Off-Policy) from scratch on OpenAI Gymnasium CliffWalking-v1 with live side-by-side animations and policy heatmaps.
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