A Nonparametric Off-Policy Policy Gradient
This addresses the inefficiency of RL in real-world scenarios like robot learning, though it appears incremental as it builds on existing off-policy approaches.
The paper tackles the high sample complexity of reinforcement learning, especially in policy gradient algorithms, by developing a nonparametric off-policy method that uses nonparametric regression and density estimation to derive closed-form value function estimates and policy gradients. Empirically, it shows better sample efficiency than state-of-the-art methods.
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The price of such inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited. We address this issue by building on the general sample efficiency of off-policy algorithms. With nonparametric regression and density estimation methods we construct a nonparametric Bellman equation in a principled manner, which allows us to obtain closed-form estimates of the value function, and to analytically express the full policy gradient. We provide a theoretical analysis of our estimate to show that it is consistent under mild smoothness assumptions and empirically show that our approach has better sample efficiency than state-of-the-art policy gradient methods.