PPO

Proximal Policy Optimization (PPO) is a popular and effective on-policy reinforcement learning algorithm. It belongs to the family of policy gradient methods, which directly optimize the policy function to maximize the expected reward. PPO addresses the challenge of balancing exploration and exploitation by limiting the size of policy updates. This is achieved through a 'trust region' constraint, ensuring that the new policy remains 'close' to the old policy. Two main variants of PPO exist: PPO-Clip and PPO-Penalty. PPO-Clip uses a clipping function to limit the ratio between the probabilities of actions under the new and old policies. PPO-Penalty adds a penalty term to the objective function that penalizes large deviations from the old policy. PPO is known for its relative simplicity, good performance, and ease of implementation, making it a widely used algorithm in various reinforcement learning applications, including robotics, game playing, and autonomous navigation. Its stability and sample efficiency compared to other policy gradient methods like TRPO have contributed to its popularity.