This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years …
The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart …
We propose a unified mechanism for achieving coordination and communication in Multi- Agent Reinforcement Learning (MARL), through rewarding agents for having causal …
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has …
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …
This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the …
Emergent communication aims for a better understanding of human language evolution and building more efficient representations. We posit that reaching these goals will require …
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are …
We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (eg, proposing\$50) and the …