FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control

TL;DR

FiDi-RL introduces a novel approach combining deep reinforcement learning with finite-difference policy search, enhancing data efficiency, stability, and performance in continuous control tasks.

Contribution

The paper proposes FiDi-RL, a new method integrating DDPG with ARS to improve data efficiency and robustness in continuous control reinforcement learning.

Findings

FiDi-RL improves performance over standard ARS.

FiDi-RL demonstrates competitive results against existing deep RL methods.

FiDi-RL enhances stability and data efficiency in continuous control tasks.

Abstract

In recent years significant progress has been made in dealing with challenging problems using reinforcement learning.Despite its great success, reinforcement learning still faces challenge in continuous control tasks. Conventional methods always compute the derivatives of the optimal goal with a costly computation resources, and are inefficient, unstable and lack of robust-ness when dealing with such tasks. Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning. The combination of both methods so as to get the best of the both has raised attention. However, most of the existing combination works adopt complex neural networks (NNs) as the policy for control. The double-edged sword of deep NNs can yield better performance, but also makes it difficult for parameter tuning and computation. To this end, in this paper we presents a novel method called FiDi-RL, which incorporates deep RL with Finite-Difference (FiDi) policy search.FiDi-RL combines Deep Deterministic Policy Gradients (DDPG)with Augment Random Search (ARS) and aims at improving the data efficiency of ARS. The empirical results show that FiDi-RL can improves the performance and stability of ARS, and provide competitive results against some existing deep reinforcement learning methods