Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper focuses on learning within the domain of simulated RoboCup soccer, which features a small set of discrete action types, each of which is parameterized with continuous variables. The best learned agents can score goals more reliably than the 2012 RoboCup champion agent. As such, this paper represents a successful extension of deep reinforcement learning to the class of parameterized action space MDPs.
Recent work on deep reinforcement learning (e.g., REF Stone 2016, Mnih et al. 2015) ) presents approaches for using deep neural networks in conjunction with reinforcement learning to solve short-horizon MDPs with continuous state spaces.
DEEP REINFORCEMENT LEARNING IN PARAMETER- IZED ACTION SPACE
Computer Science