Reinforcement learning is a machine learning training method that operates on the principles of rewarding desired behaviors and penalizing undesired ones. In a broader context, a reinforcement learning agent possesses the capability to perceive and comprehend its environment, take actions, and acquire knowledge through a process of trial and error.
This repository features a collection of projects that delve into various aspects of reinforcement learning, including the following:
-
Implementation of Learning Algorithms: Explore the practical implementation of reinforcement learning algorithms such as Reinforcement Comparison and Actor-Critic Models. These algorithms are fundamental in training agents to make decisions that maximize cumulative rewards over time.
-
Implementation of Planning Algorithms: Gain insights into planning algorithms like Policy Iteration and Value Iteration. These techniques focus on solving reinforcement learning problems through dynamic programming and optimization methods.
-
On-Policy and Off-Policy Learning Algorithms: Understand and apply on-policy and off-policy learning algorithms. On-policy methods like SARSA (State-Action-Reward-State-Action) and off-policy methods like Q-learning offer distinct approaches to learning optimal policies.
-
Addressing Challenges in Continuous Space Learning: Dive into the implementation of diverse algorithms aimed at tackling challenges related to continuous space learning. Algorithms like Deep Deterministic Policy Gradients (DDPG) and Trust Region Policy Optimization (TRPO) are designed to handle environments with continuous action spaces.
Whether you are a newcomer to reinforcement learning or an experienced practitioner, this repository provides valuable resources and projects to enhance your understanding and practical skills in this dynamic field. Each project is accompanied by detailed explanations and code examples to help you grasp the underlying concepts. If you have any questions or need further clarification on any of the topics covered, please feel free to reach out.