Search Results for author: Jianzhun Shao

Found 6 papers, 1 papers with code

Wasserstein Unsupervised Reinforcement Learning

no code implementations15 Oct 2021 Shuncheng He, Yuhang Jiang, Hongchang Zhang, Jianzhun Shao, Xiangyang Ji

These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning.

Hierarchical Reinforcement Learning reinforcement-learning +1

Reducing Conservativeness Oriented Offline Reinforcement Learning

no code implementations27 Feb 2021 Hongchang Zhang, Jianzhun Shao, Yuhang Jiang, Shuncheng He, Xiangyang Ji

In offline reinforcement learning, a policy learns to maximize cumulative rewards with a fixed collection of data.


Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning

no code implementations24 Feb 2021 Jianzhun Shao, Hongchang Zhang, Yuhang Jiang, Shuncheng He, Xiangyang Ji

Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning.

Meta-Learning Multi-agent Reinforcement Learning +3

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