In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme ...

In this paper we consider approximate policy-iteration-based reinforcement learn- ing algorithms. In order to implement a flexible function approximation ...

In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation.

Dec 5, 2018 · Abstract:We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that combines ideas from gradient-free optimization ...

Dec 8, 2008 · We propose two novel regularized policy iteration algorithms by adding L2-regularization to two widely-used policy evaluation methods: Bellman ...

PDF | In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function.

This paper proposes two novel regularized policy iteration algorithms by adding L2-regularization to two widely-used policy evaluation methods: Bellman ...

In this paper, we propose MIPI, an MI-regularized multi-agent policy iteration algorithm to improve the generalization ability of agents under unseen team ...

We study two regularization-based approximate policy iteration algorithms, namely REG-LSPI and REG-BRM, to solve reinforcement learning and planning problems in ...

We study two regularization-based approximate policy iteration algorithms, namely REG-. LSPI and REG-BRM, to solve reinforcement learning and planning ...