Add to ORKGIn this paper, a novel Multi-agent Reinforcement Learning (MARL) approach, Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multiple agents. It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure. Evaluated by multi-agent cooperation and competition tasks and traditional control tasks including OpenAI benchmarks and robot arm manipulation, MACDPP demonstrates significant superiority in learning capability and sample efficiency compared with both relate...
Many state-of-the-art cooperative multi-agent reinforcement learning (MARL) approaches, such as MADD...
peer reviewedThis paper introduces four new algorithms that can be used for tackling multi-agent rei...
For single-agent problems, Reinforcement Learning (RL) algorithms proved to be useful learning optim...
In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem ...
A growing number of real-world control problems require teams of software agents to solve a joint ta...
Despite the recent advances of deep reinforcement learning (DRL), agents trained by DRL tend to be b...
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractic...
A plethora of real world problems, such as the control of autonomous vehicles and drones, packet del...
Recent advances in Multi-agent Reinforcement Learning (MARL) have made it possible to implement vari...
Cooperative multi-agent systems (MAS) are finding applications in a wide variety of domains, includi...
Reinforcement Learning (RL) for decentralized partially observable Markov decisionprocesses (Dec-POM...
The main contributions in this thesis include the selectively decentralized method in solving multi-...
We posit a new mechanism for cooperation in multi-agent reinforcement learning (MARL) based upon an...
Multi-agent reinforcement learning for incomplete information environments has attracted extensive a...
Deep Reinforcement Learning has achieved a plenty of breakthroughs in the past decade. Motivated by ...
Many state-of-the-art cooperative multi-agent reinforcement learning (MARL) approaches, such as MADD...
peer reviewedThis paper introduces four new algorithms that can be used for tackling multi-agent rei...
For single-agent problems, Reinforcement Learning (RL) algorithms proved to be useful learning optim...
In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem ...
A growing number of real-world control problems require teams of software agents to solve a joint ta...
Despite the recent advances of deep reinforcement learning (DRL), agents trained by DRL tend to be b...
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractic...
A plethora of real world problems, such as the control of autonomous vehicles and drones, packet del...
Recent advances in Multi-agent Reinforcement Learning (MARL) have made it possible to implement vari...
Cooperative multi-agent systems (MAS) are finding applications in a wide variety of domains, includi...
Reinforcement Learning (RL) for decentralized partially observable Markov decisionprocesses (Dec-POM...
The main contributions in this thesis include the selectively decentralized method in solving multi-...
We posit a new mechanism for cooperation in multi-agent reinforcement learning (MARL) based upon an...
Multi-agent reinforcement learning for incomplete information environments has attracted extensive a...
Deep Reinforcement Learning has achieved a plenty of breakthroughs in the past decade. Motivated by ...
Many state-of-the-art cooperative multi-agent reinforcement learning (MARL) approaches, such as MADD...
peer reviewedThis paper introduces four new algorithms that can be used for tackling multi-agent rei...
For single-agent problems, Reinforcement Learning (RL) algorithms proved to be useful learning optim...