Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Explore Reinforced: Equilibrium Approximation with Reinforcement Learning

Ryan Yu, Mateusz Nowak, Qintong Xie, Michelle Yilin Feng, Peter Chin

TL;DR

The paper addresses the challenge of approximating equilibria like $CCE$ in large, multi-step stochastic environments where traditional methods struggle and standard RL lacks equilibrium guarantees. It proposes Exp3-IXrl, a hybrid algorithm that keeps the RL agent's action selection separate from the $CCE$ computation, using Exp3-IX as a third-party observer with a certainty threshold to trigger equilibrium-based decisions. Empirical results in CybORG CC2 and in stochastic and deterministic MAB tasks show faster convergence and strong performance relative to baselines, achieving PPO-level results in CC2 with moderate training. This approach broadens the applicability of equilibrium-approximation techniques to complex adversarial settings and points to adaptive certainty strategies as a promising direction for future work.

Abstract

Current approximate Coarse Correlated Equilibria (CCE) algorithms struggle with equilibrium approximation for games in large stochastic environments but are theoretically guaranteed to converge to a strong solution concept. In contrast, modern Reinforcement Learning (RL) algorithms provide faster training yet yield weaker solutions. We introduce Exp3-IXrl - a blend of RL and game-theoretic approach, separating the RL agent's action selection from the equilibrium computation while preserving the integrity of the learning process. We demonstrate that our algorithm expands the application of equilibrium approximation algorithms to new environments. Specifically, we show the improved performance in a complex and adversarial cybersecurity network environment - the Cyber Operations Research Gym - and in the classical multi-armed bandit settings.

Explore Reinforced: Equilibrium Approximation with Reinforcement Learning

TL;DR

The paper addresses the challenge of approximating equilibria like in large, multi-step stochastic environments where traditional methods struggle and standard RL lacks equilibrium guarantees. It proposes Exp3-IXrl, a hybrid algorithm that keeps the RL agent's action selection separate from the computation, using Exp3-IX as a third-party observer with a certainty threshold to trigger equilibrium-based decisions. Empirical results in CybORG CC2 and in stochastic and deterministic MAB tasks show faster convergence and strong performance relative to baselines, achieving PPO-level results in CC2 with moderate training. This approach broadens the applicability of equilibrium-approximation techniques to complex adversarial settings and points to adaptive certainty strategies as a promising direction for future work.

Abstract

Current approximate Coarse Correlated Equilibria (CCE) algorithms struggle with equilibrium approximation for games in large stochastic environments but are theoretically guaranteed to converge to a strong solution concept. In contrast, modern Reinforcement Learning (RL) algorithms provide faster training yet yield weaker solutions. We introduce Exp3-IXrl - a blend of RL and game-theoretic approach, separating the RL agent's action selection from the equilibrium computation while preserving the integrity of the learning process. We demonstrate that our algorithm expands the application of equilibrium approximation algorithms to new environments. Specifically, we show the improved performance in a complex and adversarial cybersecurity network environment - the Cyber Operations Research Gym - and in the classical multi-armed bandit settings.

Paper Structure

This paper contains 11 sections, 2 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Modern reinforcement learning algorithms
  5. Exponential-weight algorithm for Exploration and Exploitation
  6. Algorithm
  7. Experiments and Results
  8. Environments setting
  9. Experimental procedure and metrics used
  10. Results
  11. Conclusion and Future Work

Figures (2)

  • Figure 1: Algorithm overview. Exp3-IXrl is a blend of an RL and game-theoretic approach, separating the RL agent’s action selection from equilibrium computation while preserving the integrity of the learning process.
  • Figure 2: Result of our agent in the CC2 environment with a varying certainty threshold. We achieve the performance of the PPO agent with a certainty threshold of around 2750 and with only 10000 steps, demonstrating faster convergence.