research
∙
03/02/2023
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning
Handling the problem of scalability is one of the essential issues for m...
share
research
∙
03/01/2023
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning
In this paper, we propose a new mutual information framework for multi-a...
research
∙
11/28/2022
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
Constrained reinforcement learning (RL) is an area of RL whose objective...
research
∙
06/20/2022
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
In this paper, we consider cooperative multi-agent reinforcement learnin...
research
∙
06/19/2022
Robust Imitation Learning against Variations in Environment Dynamics
In this paper, we propose a robust imitation learning (IL) framework tha...
research
∙
12/10/2021
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
This paper proposes a new sequential model learning architecture to solv...
research
∙
06/19/2021
A Max-Min Entropy Framework for Reinforcement Learning
In this paper, we propose a max-min entropy framework for reinforcement ...
research
∙
12/28/2020
Training Signal Design for Sparse Channel Estimation in Intelligent Reflecting Surface-Assisted Millimeter-Wave Communication
In this paper, the problem of training signal design for intelligent ref...
research
∙
12/14/2020
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability
In this paper, a deep reinforcement learning (DRL)-based approach to the...
research
∙
06/04/2020
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning
In this paper, we propose a maximum mutual information (MMI) framework f...
research
∙
06/02/2020
Cross-Domain Imitation Learning with a Dual Structure
In this paper, we consider cross-domain imitation learning (CDIL) in whi...
research
∙
06/02/2020
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Policy entropy regularization is commonly used for better exploration in...
research
∙
01/09/2020
Population-Guided Parallel Policy Search for Reinforcement Learning
In this paper, a new population-guided parallel learning scheme is propo...
research
∙
05/07/2019
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
In importance sampling (IS)-based reinforcement learning algorithms such...
research
∙
02/18/2019
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning
In this paper, we propose a new learning technique named message-dropout...
research
∙
08/23/2018
Tomlinson-Harashima Precoding-Aided Multi-Antenna Non-Orthogonal Multiple Access
In this paper, Tomlinson-Harashima Precoding (THP) is considered for mul...
research
∙
06/30/2018