Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation

TL;DR

This paper introduces a GPU-accelerated population-based reinforcement learning framework that improves exploration and performance in robotic tasks, demonstrating superior results over traditional algorithms and successful real-world deployment.

Contribution

It presents a novel PBRL approach combined with GPU simulation, benchmarking against state-of-the-art algorithms, and showcases the first sim-to-real deployment of PBRL agents in robotics.

Findings

PBRL outperforms baseline RL algorithms in cumulative reward.

GPU acceleration enables efficient training of multiple policies.

Successful real-world deployment of PBRL in a robotic pick task.

Abstract

In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials of time-consuming experiments. This work leverages a Population-Based Reinforcement Learning (PBRL) approach and a GPU-accelerated physics simulator to enhance the exploration capabilities of RL by concurrently training multiple policies in parallel. The PBRL framework is benchmarked against three state-of-the-art RL algorithms -- PPO, SAC, and DDPG -- dynamically adjusting hyperparameters based on the performance of learning agents. The experiments are performed on four challenging tasks in Isaac Gym -- Anymal Terrain, Shadow Hand, Humanoid, Franka Nut Pick -- by analyzing the effect of population size and mutation mechanisms for hyperparameters. The results show that PBRL agents achieve superior performance, in terms of cumulative reward, compared to non-evolutionary baseline agents. Moreover, the trained agents are finally deployed in the real world for a Franka Nut Pick task. To our knowledge, this is the first sim-to-real attempt for deploying PBRL agents on real hardware. Code and videos of the learned policies are available on our project website (https://sites.google.com/view/pbrl).