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Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics

Somnath Hazra, Pallab Dasgupta, Soumyajit Dey

TL;DR

This work proposes a novel approach that integrates distributional learning with a safety-focused loss function to improve convergence in cooperative MARL tasks and suggests that incorporating safety considerations can significantly enhance learning performance in complex, multi-agent environments.

Abstract

Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving complex real-world tasks, but the inherent stochasticity and uncertainty in these environments pose substantial challenges to efficient and robust policy learning. While Distributional Reinforcement Learning has been successfully applied in single-agent settings to address risk and uncertainty, its application in MARL is substantially limited. In this work, we propose a novel approach that integrates distributional learning with a safety-focused loss function to improve convergence in cooperative MARL tasks. Specifically, we introduce a Barrier Function based loss that leverages safety metrics, identified from inherent faults in the system, into the policy learning process. This additional loss term helps mitigate risks and encourages safer exploration during the early stages of training. We evaluate our method in the StarCraft II micromanagement benchmark, where our approach demonstrates improved convergence and outperforms state-of-the-art baselines in terms of both safety and task completion. Our results suggest that incorporating safety considerations can significantly enhance learning performance in complex, multi-agent environments.

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics

TL;DR

This work proposes a novel approach that integrates distributional learning with a safety-focused loss function to improve convergence in cooperative MARL tasks and suggests that incorporating safety considerations can significantly enhance learning performance in complex, multi-agent environments.

Abstract

Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving complex real-world tasks, but the inherent stochasticity and uncertainty in these environments pose substantial challenges to efficient and robust policy learning. While Distributional Reinforcement Learning has been successfully applied in single-agent settings to address risk and uncertainty, its application in MARL is substantially limited. In this work, we propose a novel approach that integrates distributional learning with a safety-focused loss function to improve convergence in cooperative MARL tasks. Specifically, we introduce a Barrier Function based loss that leverages safety metrics, identified from inherent faults in the system, into the policy learning process. This additional loss term helps mitigate risks and encourages safer exploration during the early stages of training. We evaluate our method in the StarCraft II micromanagement benchmark, where our approach demonstrates improved convergence and outperforms state-of-the-art baselines in terms of both safety and task completion. Our results suggest that incorporating safety considerations can significantly enhance learning performance in complex, multi-agent environments.
Paper Structure (28 sections, 5 theorems, 20 equations, 13 figures, 1 table, 1 algorithm)

This paper contains 28 sections, 5 theorems, 20 equations, 13 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Decentralized POMDP
  4. Centralized Training and Decentralized Execution
  5. Distributional RL
  6. Related Work
  7. Methodology
  8. The Barrier Function Loss
  9. Optimization on Return
  10. Overall Training Objective
  11. The Local Policy Network
  12. Theoretical Analysis
  13. Safety Verification
  14. Empirical Studies
  15. StarCraft Battle Scenarios
  16. ...and 13 more sections

Key Result

theorem 1

Consider a tabular setting for our policy with direct policy parameterization, with the number of policy parameters, $m \in \Delta(\mathcal{U})^{|\mathcal{S}|}$. Let the step size for the NPG update be $\alpha = (1 - \gamma)^{1.5}/ \sqrt{|\mathcal{S}| |\mathcal{U}|T}$, where $T$ is the number of pol

Figures (13)

  • Figure 1: Integration of the safety constraints along with loss functions calculated with respect to returns, helps reduce the uncertainity, especially during the initial phase of training.
  • Figure 2: The overall framework illustrating the training objective. The calculated errors are combined using gradient manipulation and back-propagated through the central training model. References to the abbreviated notations from the text: $Z_i^t = Z_{\pi_i}(o_i^t, u_i^t)$, $Q_i^t = Q_{\pi_i}(o_i^t, u_i^t)$, $t$ is time, $A_i^t = A_{\pi_i}(o_i^t, u_i^t)$, $V_i^t = V_{\pi_i}(o_i^t)$.
  • Figure 3: Analysis of gradient manipulation.
  • Figure 4: The input layer weights of $\pi_i$ generated using a hyper-network frm previous step's return distribution.
  • Figure 5: Comparison with the state-of-the-art algorithms using the StarCraft hard and super-hard scenarios. Our method is marked as DBF (Distributional MARL with a Barrier Function based on agent casualties).
  • ...and 8 more figures

Theorems & Definitions (6)

  • Definition 1
  • theorem 1
  • theorem 2: singh2024pas
  • lemma 1: Performance Difference Lemma kakade2002approximately
  • lemma 2: Performance Improvement Bound
  • lemma 3: Upper Bound on the Optimality Gap