Robot-arm-control-with-RL

Robot arm control with Reinforcement Learning This project focuses on controlling a 7 DOF robot arm provided in the pandasgym Reacher environment using two continuous reinforcement learning algorithms: DDPG (Deep Deterministic Policy Gradients) and TD3 (Twin Delayed Deep Deterministic Policy Gradients). The technique of Hindsight Experience Replay is used to enhance the learning process of both algorithms. Continuous RL Algorithms Continuous reinforcement learning deals with environments where actions are continuous, such as the precise control of robotic arm joints or controlling the throttle of an autonomous vehicle. The primary objective is to find policies that effectively map observed states to continuous actions, ultimately optimizing the accumulation of expected rewards. Several algorithms have been specifically developed to address this challenge, including DDPG, TD3, SAC, PPO, and more. 1- DDPG (Deep Deterministic Policy Gradients) DDPG is an actor-critic algorithm designed for continuous action spaces. It combines the strengths of policy gradients and Q-learning.