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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...
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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...
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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...
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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...
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06/19/2022
Robust Imitation Learning against Variations in Environment Dynamics
In this paper, we propose a robust imitation learning (IL) framework tha...
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12/10/2021
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
This paper proposes a new sequential model learning architecture to solv...
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06/19/2021
A Max-Min Entropy Framework for Reinforcement Learning
In this paper, we propose a max-min entropy framework for reinforcement ...
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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...
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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...
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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...
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06/02/2020
Cross-Domain Imitation Learning with a Dual Structure
In this paper, we consider cross-domain imitation learning (CDIL) in whi...
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06/02/2020
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Policy entropy regularization is commonly used for better exploration in...
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01/09/2020
Population-Guided Parallel Policy Search for Reinforcement Learning
In this paper, a new population-guided parallel learning scheme is propo...
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05/07/2019
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
In importance sampling (IS)-based reinforcement learning algorithms such...
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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...
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08/23/2018
Tomlinson-Harashima Precoding-Aided Multi-Antenna Non-Orthogonal Multiple Access
In this paper, Tomlinson-Harashima Precoding (THP) is considered for mul...
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06/30/2018
