Search Results for author: Che Wang

Found 14 papers, 6 papers with code

Dynamic Fault Characteristics Evaluation in Power Grid

no code implementations28 Nov 2023 Hao Pei, Si Lin, Chuanfu Li, Che Wang, Haoming Chen, Sizhe Li

To enhance the intelligence degree in operation and maintenance, a novel method for fault detection in power grids is proposed.

Fault Detection Graph Neural Network

Knowledge Graph Construction in Power Distribution Networks

no code implementations15 Nov 2023 Xiang Li, Che Wang, Bing Li, Hao Chen, Sizhe Li

In this paper, we propose a method for knowledge graph construction in power distribution networks.

Entity Linking graph construction +1

Pre-training with Synthetic Data Helps Offline Reinforcement Learning

1 code implementation1 Oct 2023 Zecheng Wang, Che Wang, Zixuan Dong, Keith Ross

Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (Reid et al., 2022).

D4RL Q-Learning +1

On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPs

no code implementations7 Sep 2022 Zixuan Dong, Che Wang, Keith Ross

We nevertheless show that for a large class of MDPs, which includes stochastic MDPs such as blackjack and deterministic MDPs such as Go, the Q-function in MC-UCB converges almost surely to the optimal Q function.

Open-Ended Question Answering Q-Learning

VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning

1 code implementation17 Feb 2022 Che Wang, Xufang Luo, Keith Ross, Dongsheng Li

We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks.

Offline RL reinforcement-learning +1

Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance

no code implementations17 Nov 2021 Yanqiu Wu, Xinyue Chen, Che Wang, Yiming Zhang, Keith W. Ross

In particular, Truncated Quantile Critics (TQC) achieves state-of-the-art asymptotic training performance on the MuJoCo benchmark with a distributional representation of critics; and Randomized Ensemble Double Q-Learning (REDQ) achieves high sample efficiency that is competitive with state-of-the-art model-based methods using a high update-to-data ratio and target randomization.

Continuous Control Q-Learning +1

AARL: Automated Auxiliary Loss for Reinforcement Learning

no code implementations29 Sep 2021 Tairan He, Yuge Zhang, Kan Ren, Che Wang, Weinan Zhang, Dongsheng Li, Yuqing Yang

A good state representation is crucial to reinforcement learning (RL) while an ideal representation is hard to learn only with signals from the RL objective.

reinforcement-learning Reinforcement Learning (RL)

Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

6 code implementations ICLR 2021 Xinyue Chen, Che Wang, Zijian Zhou, Keith Ross

Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks.

Q-Learning

On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning

no code implementations ICLR 2022 Che Wang, Shuhan Yuan, Kai Shao, Keith Ross

A simple and natural algorithm for reinforcement learning (RL) is Monte Carlo Exploring Starts (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and the policy is improved by choosing actions that maximize the current estimate of the Q-function.

reinforcement-learning Reinforcement Learning (RL)

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

1 code implementation NeurIPS 2020 Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross

There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment.

Imitation Learning Q-Learning +2

Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

3 code implementations ICML 2020 Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross

We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic.

Continuous Control

Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning

no code implementations25 Sep 2019 Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross

The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms.

Continuous Control reinforcement-learning +1

Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past

3 code implementations10 Jun 2019 Che Wang, Keith Ross

The ERE algorithm samples more aggressively from recent experience, and also orders the updates to ensure that updates from old data do not overwrite updates from new data.

Q-Learning reinforcement-learning +1

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