PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration

1 code implementation • 16 Mar 2022 • Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang, Fazl Barez

However, we reveal sub-optimal collaborative behaviors also emerge with strong correlations, and simply maximizing the MI can, surprisingly, hinder the learning towards better collaboration.

Multi-agent Reinforcement Learning reinforcement-learning +1

Exploring Multi-dimensional Data via Subset Embedding

no code implementations • 24 Apr 2021 • Peng Xie, Wenyuan Tao, Jie Li, Wentao Huang, Siming Chen

The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings.

What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator

1 code implementation • NeurIPS 2021 • Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves, Dong Li, Changmin Yu, Hangyu Mao, Wulong Liu, Yaodong Yang, Wenyuan Tao, Li Wang

We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.

Continuous Control Contrastive Learning +3