Search Results for author: Tianpei Yang

Found 13 papers, 3 papers with code

GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis

no code implementations27 May 2022 Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li, Jianye Hao, Yang Liu

In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.

Decision Making Program Synthesis +1

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

no code implementations16 Mar 2022 Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang

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

A Survey on Interpretable Reinforcement Learning

no code implementations24 Dec 2021 Claire Glanois, Paul Weng, Matthieu Zimmer, Dong Li, Tianpei Yang, Jianye Hao, Wulong Liu

To that aim, we distinguish interpretability (as a property of a model) and explainability (as a post-hoc operation, with the intervention of a proxy) and discuss them in the context of RL with an emphasis on the former notion.

Autonomous Driving Decision Making +1

ED2: An Environment Dynamics Decomposition Framework for World Model Construction

1 code implementation6 Dec 2021 Cong Wang, Tianpei Yang, Jianye Hao, Yan Zheng, Hongyao Tang, Fazl Barez, Jinyi Liu, Jiajie Peng, Haiyin Piao, Zhixiao Sun

To reduce the model error, previous works use a single well-designed network to fit the entire environment dynamics, which treats the environment dynamics as a black box.

Model-based Reinforcement Learning

Exploration in Deep Reinforcement Learning: A Comprehensive Survey

no code implementations14 Sep 2021 Tianpei Yang, Hongyao Tang, Chenjia Bai, Jinyi Liu, Jianye Hao, Zhaopeng Meng, Peng Liu, Zhen Wang

In this paper, we conduct a comprehensive survey on existing exploration methods in DRL and deep MARL for the purpose of providing understandings and insights on the critical problems and solutions.

Autonomous Vehicles Efficient Exploration +2

Transfer among Agents: An Efficient Multiagent Transfer Learning Framework

no code implementations28 Sep 2020 Tianpei Yang, Jianye Hao, Weixun Wang, Hongyao Tang, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yujing Hu, Yingfeng Chen, Changjie Fan

In many cases, each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.

Transfer Learning

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

no code implementations19 Feb 2020 Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, Yujing Hu, Yingfeng Cheng, Changjie Fan, Weixun Wang, Wulong Liu, Zhaodong Wang, Jiajie Peng

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks.

reinforcement-learning Transfer Learning

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

no code implementations6 Sep 2019 Weixun Wang, Tianpei Yang, Yong liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao

In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.

A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents

no code implementations NeurIPS 2018 Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan

In multiagent domains, coping with non-stationary agents that change behaviors from time to time is a challenging problem, where an agent is usually required to be able to quickly detect the other agent's policy during online interaction, and then adapt its own policy accordingly.

Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning

no code implementations10 Nov 2018 Chao Yu, Tianpei Yang, Wenxuan Zhu, Dongxu Wang, Guangliang Li

Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning.


Towards Efficient Detection and Optimal Response against Sophisticated Opponents

no code implementations12 Sep 2018 Tianpei Yang, Zhaopeng Meng, Jianye Hao, Chongjie Zhang, Yan Zheng, Ze Zheng

This paper proposes a novel approach called Bayes-ToMoP which can efficiently detect the strategy of opponents using either stationary or higher-level reasoning strategies.

Multiagent Systems

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