Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation

• URL: http://arxiv.org/abs/2406.06500v1
• Date: Mon, 10 Jun 2024 17:34:44 GMT
• Title: Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation
• Authors: Mohidul Haque Mridul, Mohammad Foysal Khan, Redwan Ahmed Rizvee, Md Mosaddek Khan,
• Abstract summary: OPS-DeMo is an online algorithm that employs dynamic error decay to detect changes in opponents' policies. Our approach outperforms PPO-trained models in dynamic scenarios like the Predator-Prey setting.
• Score: 1.079960007119637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Multi-agent Reinforcement Learning (MARL), accurately perceiving opponents' strategies is essential for both cooperative and adversarial contexts, particularly within dynamic environments. While Proximal Policy Optimization (PPO) and related algorithms such as Actor-Critic with Experience Replay (ACER), Trust Region Policy Optimization (TRPO), and Deep Deterministic Policy Gradient (DDPG) perform well in single-agent, stationary environments, they suffer from high variance in MARL due to non-stationary and hidden policies of opponents, leading to diminished reward performance. Additionally, existing methods in MARL face significant challenges, including the need for inter-agent communication, reliance on explicit reward information, high computational demands, and sampling inefficiencies. These issues render them less effective in continuous environments where opponents may abruptly change their policies without prior notice. Against this background, we present OPS-DeMo (Online Policy Switch-Detection Model), an online algorithm that employs dynamic error decay to detect changes in opponents' policies. OPS-DeMo continuously updates its beliefs using an Assumed Opponent Policy (AOP) Bank and selects corresponding responses from a pre-trained Response Policy Bank. Each response policy is trained against consistently strategizing opponents, reducing training uncertainty and enabling the effective use of algorithms like PPO in multi-agent environments. Comparative assessments show that our approach outperforms PPO-trained models in dynamic scenarios like the Predator-Prey setting, providing greater robustness to sudden policy shifts and enabling more informed decision-making through precise opponent policy insights.

Related papers

• Large Language Model driven Policy Exploration for Recommender Systems [50.70228564385797]
  offline RL policies trained on static user data are vulnerable to distribution shift when deployed in dynamic online environments. Online RL-based RS also face challenges in production deployment due to the risks of exposing users to untrained or unstable policies. Large Language Models (LLMs) offer a promising solution to mimic user objectives and preferences for pre-training policies offline. We propose an Interaction-Augmented Learned Policy (iALP) that utilizes user preferences distilled from an LLM.
  arXiv  Detail & Related papers  (2025-01-23T16:37:44Z)
• Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization [8.877649895977479]
  offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. In this work, we revisit the existing offline MARL methods and show that in certain scenarios they can be problematic. We propose a new offline MARL algorithm, named In-Sample Sequential Policy Optimization (InSPO)
  arXiv  Detail & Related papers  (2024-12-10T16:19:08Z)
• OMPO: A Unified Framework for RL under Policy and Dynamics Shifts [42.57662196581823]
  Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge. Existing works often overlook the distribution discrepancies induced by policy or dynamics shifts, or rely on specialized algorithms with task priors. In this paper, we identify a unified strategy for online RL policy learning under diverse settings of policy and dynamics shifts: transition occupancy matching.
  arXiv  Detail & Related papers  (2024-05-29T13:36:36Z)
• Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization [55.97310586039358]
  Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality. We propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO) Specifically, we introduce the Q-weighted variational loss, which can be proved to be a tight lower bound of the policy objective in online RL under certain conditions. We also develop an efficient behavior policy to enhance sample efficiency by reducing the variance of the diffusion policy during online interactions.
  arXiv  Detail & Related papers  (2024-05-25T10:45:46Z)
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution [60.6953713877886]
  Policy Convolution family of estimators uses latent structure within actions to strategically convolve the logging and target policies. Experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC.
  arXiv  Detail & Related papers  (2023-10-24T01:00:01Z)
• Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning [139.53668999720605]
  We present a multi-agent PPO algorithm in which the local policy of each agent is updated similarly to vanilla PPO. We prove that with standard regularity conditions on the Markov game and problem-dependent quantities, our algorithm converges to the globally optimal policy at a sublinear rate.
  arXiv  Detail & Related papers  (2023-05-08T16:20:03Z)
• Adversarial Policy Optimization in Deep Reinforcement Learning [16.999444076456268]
  The policy represented by the deep neural network can overfitting, which hamper a reinforcement learning agent from learning effective policy. Data augmentation can provide a performance boost to RL agents by mitigating the effect of overfitting. We propose a novel RL algorithm to mitigate the above issue and improve the efficiency of the learned policy.
  arXiv  Detail & Related papers  (2023-04-27T21:01:08Z)
• Policy Dispersion in Non-Markovian Environment [53.05904889617441]
  This paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment. We first adopt a transformer-based method to learn policy embeddings. Then, we stack the policy embeddings to construct a dispersion matrix to induce a set of diverse policies.
  arXiv  Detail & Related papers  (2023-02-28T11:58:39Z)
• Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method [6.261762915564555]
  We discuss the problem of decentralized multi-agent reinforcement learning (MARL) in this work. In our setting, the global state, action, and reward are assumed to be fully observable, while the local policy is protected as privacy by each agent, and thus cannot be shared with others. The policy evaluation and policy improvement algorithms are designed for discrete and continuous state-action-space Markov Decision Process (MDP) respectively.
  arXiv  Detail & Related papers  (2021-10-31T09:08:46Z)
• Variational Policy Propagation for Multi-agent Reinforcement Learning [68.26579560607597]
  We propose a emphcollaborative multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a emphjoint policy through the interactions over agents. We prove that the joint policy is a Markov Random Field under some mild conditions, which in turn reduces the policy space effectively. We integrate the variational inference as special differentiable layers in policy such as the actions can be efficiently sampled from the Markov Random Field and the overall policy is differentiable.
  arXiv  Detail & Related papers  (2020-04-19T15:42:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.