Reward-Free Reinforcement Learning Algorithm Using Prediction Network

Abstract

In general reinforcement learning tasks, the formulation of reward functions is a very important step in reinforcement learning. The reward function is not easy to formulate in a large number of systems. The network training effect is sensitive to the reward function, and different reward value functions will get different results. For a class of systems that meet specific conditions, the traditional reinforcement learning method is improved. A state quantity function is designed to replace the reward function, which is more efficient than the traditional reward function. At the same time, the predictive network link is designed so that the network can learn the value of the general state by using the special state. The overall structure of the network will be improved based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Finally, the algorithm was successfully applied in the environment of FrozenLake, and achieved good performance. The experiment proves the effectiveness of the algorithm and realizes rewardless reinforcement learning in a class of systems.