Long Yang | DeepAI

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∙ 06/05/2023

A General Perspective on Objectives of Reinforcement Learning

In this lecture, we present a general perspective on reinforcement learn...

0 Long Yang, et al. ∙

∙ 05/22/2023

Policy Representation via Diffusion Probability Model for Reinforcement Learning

Popular reinforcement learning (RL) algorithms tend to produce a unimoda...

0 Long Yang, et al. ∙

∙ 01/25/2023

Energy-Delay Tradeoff in Helper-Assisted NOMA-MEC Systems: A Four-Sided Matching Algorithm

This paper designs a helper-assisted resource allocation strategy in non...

0 Mengmeng Ren, et al. ∙

∙ 08/30/2022

Optimal Probabilistic Constellation Shaping for Covert Communications

In this paper, we investigate the optimal probabilistic constellation sh...

0 Shuai Ma, et al. ∙

∙ 05/24/2022

Penalized Proximal Policy Optimization for Safe Reinforcement Learning

Safe reinforcement learning aims to learn the optimal policy while satis...

16 Linrui zhang, et al. ∙

∙ 06/15/2021

Thompson Sampling for Unimodal Bandits

In this paper, we propose a Thompson Sampling algorithm for unimodal ban...

0 Long Yang, et al. ∙

∙ 12/02/2020

Sample Complexity of Policy Gradient Finding Second-Order Stationary Points

The goal of policy-based reinforcement learning (RL) is to search the ma...

0 Long Yang, et al. ∙

∙ 09/06/2019

Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning

Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sa...

0 Long Yang, et al. ∙

∙ 07/01/2019

FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control

In recent years significant progress has been made in dealing with chall...

5 Longxiang Shi, et al. ∙

∙ 06/25/2019

Policy Optimization with Stochastic Mirror Descent

Stochastic mirror descent (SMD) keeps the advantages of simplicity of im...

16 Long Yang, et al. ∙

∙ 06/25/2019

Expected Sarsa(λ) with Control Variate for Variance Reduction

Off-policy learning is powerful for reinforcement learning. However, the...

0 Long Yang, et al. ∙

∙ 05/17/2019

TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning

Off-policy reinforcement learning with eligibility traces is challenging...

0 Longxiang Shi, et al. ∙

∙ 08/01/2018

Beetle Swarm Optimization Algorithm:Theory and Application

In this paper, a new meta-heuristic algorithm, called beetle swarm optim...

0 Tiantian Wang, et al. ∙

∙ 06/14/2018

Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network

In this paper, we focus on policy discrepancy in return-based deep Q-net...

0 Wenjia Meng, et al. ∙

∙ 02/09/2018

A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning

Recently, a new multi-step temporal learning algorithm, called Q(σ), uni...

0 Long Yang, et al. ∙