Improved SARSA and DQN algorithms for reinforcement learning

Abstract

Reinforcement learning is a branch of machine learning in which an agent interacts with an environment to learn optimal actions that maximize cumulative rewards. This paper aims to enhance the SARSA and DQN algorithms in four key aspects: the ε-greedy policy, reward function, value iteration approach, and sampling probability. The experiments are conducted in three scenarios: path planning, CartPole, and MountainCar. The results show that, in these environments, the improved algorithms exhibit better convergence, higher rewards, and more stable training processes.