TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning

TL;DR

TOP-ERL introduces a transformer-based off-policy reinforcement learning algorithm that improves sample efficiency and stability in long-horizon, trajectory-based policy learning, especially in robot environments.

Contribution

It pioneers the integration of transformer architectures into off-policy ERL, enabling effective evaluation of entire action sequences for improved learning.

Findings

Outperforms state-of-the-art RL methods in robot environments

Demonstrates stable and efficient training with long action trajectories

Ablation studies highlight the importance of key design choices

Abstract

This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP), allowing for smooth and efficient exploration over long horizons while capturing high-level temporal correlations. However, ERL methods are often constrained to on-policy frameworks due to the difficulty of evaluating state-action values for entire action sequences, limiting their sample efficiency and preventing the use of more efficient off-policy architectures. TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation. These contributions result in efficient and stable training that is reflected in the empirical results conducted on sophisticated robot learning environments. TOP-ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model performance.