关键词: 强化学习, 演化算法, 从演示中学习, 神经网络

Abstract: Reinforcement learning aims at learning an optimal policy that maximizes the long term rewards, from interactions with the environment. Since the environment feedbacks commonly delay after a sequences of actions, reinforcement learning has to tackle the problem of searching in a huge policy space, and thus an effective search is the key to a success approach. Previous studies explore various ways to achieve effective search methods, one effective way is employing the evolutionary algorithm as the search method, and another direction is introducing user demonstration data to guide the search. In this work, it investigates the combination of the two directions, and proposes the iNEAT+Q approach, which trains a neural network using the demonstration data as well as integrating the demonstration data into the fitness function for the evolutionary algorithm. Preliminary empirical study shows that iNEAT+Q is superior to NEAT+Q, which is an classical evolutionary reinforcement learning approach.

Key words: reinforcement learning, evolutionary algorithm, learning from demonstrations, neural network