Deep contract design via discontinuous networks
Contract design involves a principal who establishes contractual agreements about
payments for outcomes that arise from the actions of an agent. In this paper, we initiate the …
payments for outcomes that arise from the actions of an agent. In this paper, we initiate the …
Towards a better understanding of learning with multiagent teams
While it has long been recognized that a team of individual learning agents can be greater
than the sum of its parts, recent work has shown that larger teams are not necessarily more …
than the sum of its parts, recent work has shown that larger teams are not necessarily more …
The bandit whisperer: Communication learning for restless bandits
Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a
promising avenue for addressing allocation problems with resource constraints and …
promising avenue for addressing allocation problems with resource constraints and …
Learning Optimal" Pigovian Tax" in Sequential Social Dilemmas
In multi-agent reinforcement learning, each agent acts to maximize its individual
accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect …
accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect …
Learning roles with emergent social value orientations
Social dilemmas can be considered situations where individual rationality leads to collective
irrationality. The multi-agent reinforcement learning community has leveraged ideas from …
irrationality. The multi-agent reinforcement learning community has leveraged ideas from …
GHQ: grouped hybrid Q-learning for cooperative heterogeneous multi-agent reinforcement learning
Previous deep multi-agent reinforcement learning (MARL) algorithms have achieved
impressive results, typically in symmetric and homogeneous scenarios. However …
impressive results, typically in symmetric and homogeneous scenarios. However …
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning
Previous deep multi-agent reinforcement learning (MARL) algorithms have achieved
impressive results, typically in homogeneous scenarios. However, heterogeneous scenarios …
impressive results, typically in homogeneous scenarios. However, heterogeneous scenarios …