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Enhancing Reinforcement Learning Agents with Local Guides

Paul Daoudi, Bogdan Robu, Christophe Prieur, Ludovic Dos Santos, Merwan Barlier

TL;DR

The paper tackles sample-efficient reinforcement learning in safety-critical settings by introducing Reinforcement Learning with Local Guides (RLLG), which leverages region-specific guidance via a local policy $\pi_g(\cdot|s)$ and a confidence function $\lambda(s)$ within an Approximate Policy Iteration framework. It analyzes three baseline integration strategies—Strict Action Guided (SAG), Reward Guided (RG), and Policy Improvement Guided (PIG)—and proposes Perturbed Action Guided (PAG), which combines a strong initialization from the local guide with a learnable perturbation to surpass its limitations. The authors provide a rigorous empirical evaluation against SAC across attractive and repulsive guide scenarios, showing that PAG achieves faster early learning and safer exploration, with robust performance across hyper-parameter choices. The work offers a practical path to deploying RL in real-world systems where unsafe exploration must be avoided and sample efficiency is crucial, by enabling local expertise to guide global learning without requiring a perfect global guide. It also highlights limitations related to confidence estimation and discrete-action extensions as avenues for future research.

Abstract

This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent. For this, we show how to adapt existing algorithms to this setting before introducing a novel algorithm based on a noisy policy-switching procedure. This approach builds on a proper Approximate Policy Evaluation (APE) scheme to provide a perturbation that carefully leads the local guides towards better actions. We evaluated our method on a set of classical Reinforcement Learning problems, including safety-critical systems where the agent cannot enter some areas at the risk of triggering catastrophic consequences. In all the proposed environments, our agent proved to be efficient at leveraging those policies to improve the performance of any APE-based Reinforcement Learning algorithm, especially in its first learning stages.

Enhancing Reinforcement Learning Agents with Local Guides

TL;DR

The paper tackles sample-efficient reinforcement learning in safety-critical settings by introducing Reinforcement Learning with Local Guides (RLLG), which leverages region-specific guidance via a local policy and a confidence function within an Approximate Policy Iteration framework. It analyzes three baseline integration strategies—Strict Action Guided (SAG), Reward Guided (RG), and Policy Improvement Guided (PIG)—and proposes Perturbed Action Guided (PAG), which combines a strong initialization from the local guide with a learnable perturbation to surpass its limitations. The authors provide a rigorous empirical evaluation against SAC across attractive and repulsive guide scenarios, showing that PAG achieves faster early learning and safer exploration, with robust performance across hyper-parameter choices. The work offers a practical path to deploying RL in real-world systems where unsafe exploration must be avoided and sample efficiency is crucial, by enabling local expertise to guide global learning without requiring a perfect global guide. It also highlights limitations related to confidence estimation and discrete-action extensions as avenues for future research.

Abstract

This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent. For this, we show how to adapt existing algorithms to this setting before introducing a novel algorithm based on a noisy policy-switching procedure. This approach builds on a proper Approximate Policy Evaluation (APE) scheme to provide a perturbation that carefully leads the local guides towards better actions. We evaluated our method on a set of classical Reinforcement Learning problems, including safety-critical systems where the agent cannot enter some areas at the risk of triggering catastrophic consequences. In all the proposed environments, our agent proved to be efficient at leveraging those policies to improve the performance of any APE-based Reinforcement Learning algorithm, especially in its first learning stages.
Paper Structure (44 sections, 10 equations, 8 figures, 2 tables, 1 algorithm)

This paper contains 44 sections, 10 equations, 8 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Imitation Learning with Demonstrations
  4. Reinforcement Learning from Demonstrations (RLfD)
  5. Reward Shaping
  6. Reinforcement Learning with a Global Guide
  7. Local Expertise
  8. Preliminaries and Problem Setting
  9. Preliminaries
  10. Approximate Policy Iteration
  11. Problem setting
  12. Reinforcement Learning with Local Guides
  13. Classical integration of the local controller
  14. Strict Action Guided (SAG)
  15. Reward Guided (RG)
  16. ...and 29 more sections

Figures (8)

  • Figure 1: Reinforcement Learning with Local Guides
  • Figure 2: Environment visualizations.
  • Figure 3: Hyper-parameter analysis of PIG (top) and PAG (bottom) on environments with attractive policies.
  • Figure 4: Overall performances comparing PAG with SAC, SAG and PIG on 3 different environments with attractive policies.
  • Figure 5: Hyper-parameter analysis of PIG (top) and PAG (bottom) on environments with repulsive policies.
  • ...and 3 more figures