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Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning
We investigate how reinforcement learning agents can learn to cooperate....
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Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning
We present an effective technique for training deep learning agents capa...
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M^3RL: Mind-aware Multi-agent Management Reinforcement Learning
Most of the prior work on multi-agent reinforcement learning (MARL) achi...
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Computational Rationalization: The Inverse Equilibrium Problem
Modeling the purposeful behavior of imperfect agents from a small number...
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Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games
This paper presents an algorithmic framework for learning robust policie...
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Do deep reinforcement learning agents model intentions?
Inferring other agents' mental states such as their knowledge, beliefs a...
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Learning Nested Agent Models in an Information Economy
We present our approach to the problem of how an agent, within an econom...
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Modeling Others using Oneself in Multi-Agent Reinforcement Learning
We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks. We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner. We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.
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