Abstract: In recent years, reinforcement learning (RL) has emerged as a solution ... Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained ...

This project aims to implement and compare three Reinforcement Learning (RL) algorithms, Deep Q Networks (DQN), Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO), using PyTorch. The ...

Policy gradient methods are a class of RL algorithms that optimize the agent's policy, which is a function that maps states to actions. Proximal policy optimization (PPO) is a popular and ...

This project employs Proximal Policy Optimization (PPO), a state-of-the-art policy ... making it a practical choice for a wide range of RL tasks. The algorithm's on-policy nature means it learns ...

Initially designed for continuous control tasks, Proximal Policy ... Are there simpler algorithms that scale to modern RL applications? Policy Gradient (PG) methods, renowned for their direct, ...

However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice. Inspired by this, we propose an efficient RL algorithm, called {\em mirror ...