Search Results for author: Yat Long Lo

Found 6 papers, 0 papers with code

Improving International Climate Policy via Mutually Conditional Binding Commitments

no code implementations26 Jul 2023 Jobst Heitzig, Jörg Oechssler, Christoph Pröschel, Niranjana Ragavan, Yat Long Lo

The Paris Agreement, considered a significant milestone in climate negotiations, has faced challenges in effectively addressing climate change due to the unconditional nature of most Nationally Determined Contributions (NDCs).

Learning Multi-Agent Communication with Contrastive Learning

no code implementations3 Jul 2023 Yat Long Lo, Biswa Sengupta, Jakob Foerster, Michael Noukhovitch

By examining the relationship between messages sent and received, we propose to learn to communicate using contrastive learning to maximize the mutual information between messages of a given trajectory.

Contrastive Learning

Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning

no code implementations19 Mar 2023 Yat Long Lo, Christian Schroeder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson

By enabling agents to communicate, recent cooperative multi-agent reinforcement learning (MARL) methods have demonstrated better task performance and more coordinated behavior.

Multi-agent Reinforcement Learning reinforcement-learning +1

Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learning

no code implementations7 Mar 2022 Yat Long Lo, Biswa Sengupta

For communication to happen successfully, a common language is required between agents to understand information communicated by one another.

Contrastive Learning

Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks

no code implementations16 Mar 2020 Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White

Unfortunately, the performance of deep reinforcement learning systems is sensitive to hyper-parameter settings and architecture choices.

reinforcement-learning Reinforcement Learning (RL)

Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps

no code implementations29 Oct 2019 Yat Long Lo, Sina Ghiassian

Yet, neural networks tend to forget what they learned in the past, especially when they learn online and fully incrementally, a setting in which the weights are updated after each sample is received and the sample is then discarded.

reinforcement-learning Reinforcement Learning (RL)

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