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Learning to communicate through imagination with model-based deep multi-agent reinforcement learning

2021-01-01 · Arnu Pretorius, Scott Cameron, Andries Petrus Smit, Elan van Biljon, Lawrence Francis, Femi Azeez, Alexandre Laterre, Karim Beguir

The human imagination is an integral component of our intelligence. Furthermore, the core utility of our imagination is deeply coupled with communication. Language, argued to have been developed through complex interaction within growing collective societies serves as an instruction to the imagination, giving us the ability to share abstract mental representations and perform joint spatiotemporal planning. In this paper, we explore communication through imagination with multi-agent reinforcement learning. Specifically, we develop a model-based approach where agents jointly plan through recurrent communication of their respective predictions of the future. Each agent has access to a learned world model capable of producing model rollouts of future states and predicted rewards, conditioned on the actions sampled from the agent's policy. These rollouts are then encoded into messages and used to learn a communication protocol during training via differentiable message passing. We highlight the benefits of our model-based approach, compared to a set of strong baselines, by developing a set of specialised experiments using novel as well as well-known multi-agent environments.

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Learning Predictive Communication by Imagination in Networked System Control

2021-01-01 · Yali Du, Yifan Zhao, Meng Fang, Jun Wang 외

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Do Agents Dream of Electric Sheep?: Improving Generalization in Reinforcement Learning through Generative Learning

2024-03-12 · Giorgio Franceschelli, Mirco Musolesi

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Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language

2021-07-20 · Kevin Eloff, Herman A. Engelbrecht

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Dream to Control: Learning Behaviors by Latent Imagination

2019-12-03 · ICLR 2020 1 · Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi

Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many p…

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Imagination-Augmented Agents for Deep Reinforcement Learning

2017-07-19 · NeurIPS 2017 12 · Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing 외

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and pla…

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