Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Applying the policy gradient method to behavior learning in multiagent systems: The pursuit problem
Systems and Computers in Japan
Policy gradient reinforcement learning with environmental dynamics and action-values in policies
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
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Policy gradient methods are very useful approaches in reinforcement learning. In our policy gradient approach to behavior learning of agents, we define an agent's decision problem at each time step as a problem of minimizing an objective function. In this paper, we give an objective function that consists of two types of parameters representing environmental dynamics and state-value functions. We derive separate learning rules for the two types of parameters so that the two sets of parameters can be learned independently. Separating these two types of parameters will make it possible to reuse state-value functions for agents in other different environmental dynamics, even if the dynamics is stochastic. Our simulation experiments on learning hunter-agent policies in pursuit problems show the effectiveness of our method.