Value-decomposition networks for cooperative multi-agent learning
We study the problem of cooperative multi-agent reinforcement learning with a single joint
reward signal. This class of learning problems is difficult because of the often large …
reward signal. This class of learning problems is difficult because of the often large …
A unified game-theoretic approach to multiagent reinforcement learning
There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …
partly to the recent success of deep neural networks. The simplest form of MARL is …
Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks
As urban traffic condition is diverse and complicated, applying reinforcement learning to
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
Emergent coordination through competition
We study the emergence of cooperative behaviors in reinforcement learning agents by
introducing a challenging competitive multi-agent soccer environment with continuous …
introducing a challenging competitive multi-agent soccer environment with continuous …
Cooperative control for multi-player pursuit-evasion games with reinforcement learning
In this paper, we consider a pursuit-evasion game in which multiple pursuers attempt to
capture one superior evader. A distributed cooperative pursuit strategy with communication …
capture one superior evader. A distributed cooperative pursuit strategy with communication …
Multi-agent reinforcement learning for order-dispatching via order-vehicle distribution matching
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-
hailing systems. Most of the existing solutions for order-dispatching are centralized …
hailing systems. Most of the existing solutions for order-dispatching are centralized …
Biases for emergent communication in multi-agent reinforcement learning
We study the problem of emergent communication, in which language arises because
speakers and listeners must communicate information in order to solve tasks. In temporally …
speakers and listeners must communicate information in order to solve tasks. In temporally …
Randomized entity-wise factorization for multi-agent reinforcement learning
Multi-agent settings in the real world often involve tasks with varying types and quantities of
agents and non-agent entities; however, common patterns of behavior often emerge among …
agents and non-agent entities; however, common patterns of behavior often emerge among …
Certifiably robust policy learning against adversarial multi-agent communication
Communication is important in many multi-agent reinforcement learning (MARL) problems
for agents to share information and make good decisions. However, when deploying trained …
for agents to share information and make good decisions. However, when deploying trained …
Emergent communication under varying sizes and connectivities
Recent advances in deep neural networks allowed artificial agents to derive their own
emergent languages that promote interaction, coordination, and collaboration within a …
emergent languages that promote interaction, coordination, and collaboration within a …