Agent-time attention for sparse rewards multi-agent reinforcement learning
Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This
challenge is amplified in multi-agent reinforcement learning (MARL) where credit …
challenge is amplified in multi-agent reinforcement learning (MARL) where credit …
STAS: Spatial-Temporal Return Decomposition for Solving Sparse Rewards Problems in Multi-agent Reinforcement Learning
Centralized Training with Decentralized Execution (CTDE) has been proven to be an
effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the …
effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the …
RevAP: A bankruptcy-based algorithm to solve the multi-agent credit assignment problem in task start threshold-based multi-agent systems
Abstract Multi-Agent Systems (MASs) are the prominent symbol of Distributed Artificial
Intelligence (DAI). Learning in MAS, which is commonly based on Reinforcement Learning …
Intelligence (DAI). Learning in MAS, which is commonly based on Reinforcement Learning …
Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world
applications, even with only episodic rewards. Previous approaches have made some …
applications, even with only episodic rewards. Previous approaches have made some …
Learning Individual Potential-Based Rewards in Multi-Agent Reinforcement Learning
C Yang, P Xu, J Zhang - IEEE Transactions on Games, 2024 - ieeexplore.ieee.org
A great challenge for applying multi-agent reinforcement learning (MARL) in the field of
game AI is to enable agents to learn diversified policies to handle different gamespecific …
game AI is to enable agents to learn diversified policies to handle different gamespecific …
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Centralized Training with Decentralized Execution (CTDE) has been proven to be an
effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the …
effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the …
Graph Q-Learning for Combinatorial Optimization
Graph-structured data is ubiquitous throughout natural and social sciences, and Graph
Neural Networks (GNNs) have recently been shown to be effective at solving prediction and …
Neural Networks (GNNs) have recently been shown to be effective at solving prediction and …
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
In multi-agent environments, agents often struggle to learn optimal policies due to sparse or
delayed global rewards, particularly in long-horizon tasks where it is challenging to evaluate …
delayed global rewards, particularly in long-horizon tasks where it is challenging to evaluate …
GOV-REK: Governed Reward Engineering Kernels for designing robust multi-agent reinforcement learning systems
A Rana, M Oesterle, J Brinkmann - arXiv preprint arXiv:2404.01131, 2024 - arxiv.org
For multi-agent reinforcement learning systems (MARLS), the problem formulation generally
involves investing massive reward engineering effort specific to a given problem. However …
involves investing massive reward engineering effort specific to a given problem. However …
Classifying ambiguous identities in hidden-role Stochastic games with multi-agent reinforcement learning
Multi-agent reinforcement learning (MARL) is a prevalent learning paradigm for solving
stochastic games. In most MARL studies, agents in a game are defined as teammates or …
stochastic games. In most MARL studies, agents in a game are defined as teammates or …