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This paper proposes GProp, a deep reinforcement learning algorithm forcontinuous policies with compatible function approximation. The algorithm isbased on two innovations. Firstly, we present a temporal-difference basedmethod for learning the gradient of the value-function. Secondly, we presentthe deviator-actor-critic (DAC) model, which comprises three neural networksthat estimate the value function, its gradient, and determine the actor'spolicy respectively. We evaluate GProp on two challenging tasks: a contextualbandit problem constructed from nonparametric regression datasets that isdesigned to probe the ability of reinforcement learning algorithms toaccurately estimate gradients; and the octopus arm, a challenging reinforcementlearning benchmark. GProp is competitive with fully supervised methods on thebandit task and achieves the best performance to date on the octopus arm.

Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies