Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge

TL;DR

This paper introduces an advanced RDPG-based deep learning framework for bipedal robot locomotion on rough terrain, addressing partial observability and demonstrating superior adaptability and success in simulation.

Contribution

The paper proposes three key improvements to RDPG for partial observability, enabling effective bipedal locomotion on rugged terrains in simulation.

Findings

RDPG framework achieves higher success rates in rugged terrain traversal.

The method adapts effectively to various obstacles.

Simulation results outperform existing approaches.

Abstract

This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled error measure and its variance induced by the partial observability of environment and subtrajectory sampling, respectively. Three major improvements are introduced in our RDPG based learning framework: tail-step bootstrap of interpolated temporal difference, initialisation of hidden state using past trajectory scanning, and injection of external experiences learned by other agents. The proposed learning framework was implemented to solve the Bipedal-Walker challenge in OpenAI's gym simulation environment where only partial state information is available. Our simulation study shows that the autonomous behaviors generated by the RDPG agent are highly adaptive to a variety of obstacles and enables the agent to effectively traverse rugged terrains for long distance with higher success rate than leading contenders.