Attention-guided contrastive role representations for multi-agent reinforcement learning
Real-world multi-agent tasks usually involve dynamic team composition with the emergence
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
Confidence-Based Curriculum Learning for Multi-Agent Path Finding
A wide range of real-world applications can be formulated as Multi-Agent Path Finding
(MAPF) problem, where the goal is to find collision-free paths for multiple agents with …
(MAPF) problem, where the goal is to find collision-free paths for multiple agents with …
Towards Fault Tolerance in Multi-Agent Reinforcement Learning
Y Shi, H Pei, L Feng, Y Zhang, D Yao - arxiv preprint arxiv:2412.00534, 2024 - arxiv.org
Agent faults pose a significant threat to the performance of multi-agent reinforcement
learning (MARL) algorithms, introducing two key challenges. First, agents often struggle to …
learning (MARL) algorithms, introducing two key challenges. First, agents often struggle to …
Farthest Agent Selection With Episode-Wise Observations for Real-Time Multi-Agent Reinforcement Learning Applications
Multi-agent reinforcement learning (MARL) algorithms have been widely used for many
applications requiring sequential decision-making to maximize the expected rewards …
applications requiring sequential decision-making to maximize the expected rewards …
[PDF][PDF] Attention-Based Recurrency for Multi-Agent Reinforcement Learning under State Uncertainty.
State uncertainty poses a major challenge for decentralized coordination, where multiple
agents act according to noisy observations without any access to other agents' information …
agents act according to noisy observations without any access to other agents' information …
Confidence-Based Curricula for Multi-Agent Path Finding via Reinforcement Learning
A wide range of real-world applications can be formulated as Multi-Agent Path Finding
(MAPF) problem, where the goal is to find collision-free paths for multiple agents with …
(MAPF) problem, where the goal is to find collision-free paths for multiple agents with …
Priority Over Quantity: A Self-Incentive Credit Assignment Scheme for Cooperative Multiagent Reinforcement Learning
H Tang, C Wang, S Chang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Centralized training and decentralized execution (CTDE) paradigm is widely employed to
address the nonstationary and partial observability in multiagent reinforcement learning …
address the nonstationary and partial observability in multiagent reinforcement learning …
[HTML][HTML] A Multi-Agent Centralized Strategy Gradient Reinforcement Learning Algorithm Based on State Transition
L Sheng, H Chen, X Chen - Algorithms, 2024 - mdpi.com
The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep
Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge …
Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge …
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
This study proposes the use of a social learning method to estimate a global state within a
multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating in a …
multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating in a …
Swarm Behavior Cloning
In sequential decision-making environments, the primary approaches for training agents are
Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on …
Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on …