Project Descriptions

Reinforcement Learning (RL) is a subfield of machine learning that focuses on training agents to make sequential decisions in an environment to maximize a cumulative reward signal. The goal of RL is to develop algorithms that can learn to make intelligent decisions through trial and error, much like how humans and animals learn from experience. RL has gained a lot of attention in recent years due to its ability to solve complex decision-making problems, ranging from playing video games to controlling robotic systems.

In RL, an agent interacts with an environment by taking actions and receiving rewards based on those actions. The agent's goal is to learn a policy that maximizes its expected cumulative reward over time. The policy is essentially a mapping from states to actions, which the agent uses to decide what action to take in each state.

One of the key challenges in RL is balancing exploration and exploitation. The agent needs to explore the environment to discover the optimal policy, but at the same time, it needs to exploit its current knowledge to maximize its reward. There are several algorithms in RL that address this challenge, including Q-Learning, Policy Gradient Methods, and Actor-Critic Methods.

RL has a wide range of applications in various domains, including robotics, finance, and gaming. Some examples of RL applications include training robots to perform tasks like grasping and manipulation, optimizing investment portfolios, and developing intelligent agents to play games like chess and Go.

This repository is for learning and implementing various popular reinforcement learning techniques. I'll utilize the gym library, which is a widely used library for developing and comparing reinforcement learning algorithms. Gym is a popular open-source library designed specifically for reinforcement learning tasks. It provides a wide range of environments, such as classic control tasks, Atari games, robotics, and more, that users can use to train and test their reinforcement learning agents. Additionally, Gym provides a simple and standardized interface for interacting with these environments, making it easy to experiment with different RL algorithms and compare their performance.