PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their actions. In such multi-agent environments, additional learning problems arise due to the continually changing decision-making policies of agents. This paper surveys recent works that address the non-stationarity problem in multi-agent deep reinforcement learning. The surveyed methods range from modifications in the training procedure, such as centralized training, to learning representations of the opponent's policy, meta-learning, communication, and decentralized learning. The survey concludes with a list of open problems and possible lines of future research.
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Temporal diversity in task distribution during training increases generalization bias over memorization in transformers for in-context linear regression.
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
RE-SAC disentangles aleatoric and epistemic risks via IPM regularization on the critic and a diversified Q-ensemble, yielding higher rewards and lower estimation error than vanilla SAC in simulated bus corridor control.
PIMbot introduces an adaptive attack using reward-channel and policy manipulation to disrupt cooperation in multi-robot social dilemma RL, shown effective in Gazebo simulation and on NVIDIA Jetson hardware.
citing papers explorer
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Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks
PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
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Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling
Temporal diversity in task distribution during training increases generalization bias over memorization in transformers for in-context linear regression.
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Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
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ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
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RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach
RE-SAC disentangles aleatoric and epistemic risks via IPM regularization on the critic and a diversified Q-ensemble, yielding higher rewards and lower estimation error than vanilla SAC in simulated bus corridor control.
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PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
PIMbot introduces an adaptive attack using reward-channel and policy manipulation to disrupt cooperation in multi-robot social dilemma RL, shown effective in Gazebo simulation and on NVIDIA Jetson hardware.