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08/14/2023
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse
Humans have the ability to reuse previously learned policies to solve ne...
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07/06/2023
Learning to Solve Tasks with Exploring Prior Behaviours
Demonstrations are widely used in Deep Reinforcement Learning (DRL) for ...
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05/31/2023
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
Recent offline meta-reinforcement learning (meta-RL) methods typically u...
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02/27/2023
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
Self-supervised methods have become crucial for advancing deep learning ...
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12/01/2022
Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System
With deep reinforcement learning (RL) systems like autonomous driving be...
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10/15/2022
CUP: Critic-Guided Policy Reuse
The ability to reuse previous policies is an important aspect of human i...
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07/12/2022
Towards Global Optimality in Cooperative MARL with Sequential Transformation
Policy learning in multi-agent reinforcement learning (MARL) is challeng...
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06/07/2022
On the Role of Discount Factor in Offline Reinforcement Learning
Offline reinforcement learning (RL) enables effective learning from prev...
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01/25/2022
MOORe: Model-based Offline-to-Online Reinforcement Learning
With the success of offline reinforcement learning (RL), offline trained...
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10/19/2021
Offline Reinforcement Learning with Value-based Episodic Memory
Offline reinforcement learning (RL) shows promise of applying RL to real...
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10/01/2021
Offline Reinforcement Learning with Reverse Model-based Imagination
In offline reinforcement learning (offline RL), one of the main challeng...
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09/29/2021
On the Estimation Bias in Double Q-Learning
Double Q-learning is a classical method for reducing overestimation bias...
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09/26/2021
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates
In cooperative multi-agent reinforcement learning (MARL), where agents o...
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05/31/2021
Efficient Hierarchical Exploration with Stable Subgoal Representation Learning
Goal-conditioned hierarchical reinforcement learning (HRL) serves as a s...
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03/11/2021
Generalizable Episodic Memory for Deep Reinforcement Learning
Episodic memory-based methods can rapidly latch onto past successful str...
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12/06/2020
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
The development of deep reinforcement learning (DRL) has benefited from ...
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10/23/2020
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Sample efficiency has been one of the major challenges for deep reinforc...
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08/03/2020
QPLEX: Duplex Dueling Multi-Agent Q-Learning
We explore value-based multi-agent reinforcement learning (MARL) in the ...
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06/25/2020
SOAC: The Soft Option Actor-Critic Architecture
The option framework has shown great promise by automatically extracting...
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06/15/2020
Learn to Effectively Explore in Context-Based Meta-RL
Meta reinforcement learning (meta-RL) provides a principled approach for...
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05/31/2020
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning
Value decomposition is a popular and promising approach to scaling up mu...
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03/18/2020
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
The role concept provides a useful tool to design and understand complex...
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03/18/2020
Multi-Agent Reinforcement Learning with Emergent Roles
The role concept provides a useful tool to design and understand complex...
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10/12/2019
Influence-Based Multi-Agent Exploration
Intrinsically motivated reinforcement learning aims to address the explo...
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10/11/2019
Learning Nearly Decomposable Value Functions Via Communication Minimization
Reinforcement learning encounters major challenges in multi-agent settin...
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10/10/2019
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards
Hierarchical Reinforcement Learning (HRL) is a promising approach to sol...
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04/16/2019
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Object-based approaches for learning action-conditioned dynamics has dem...
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03/07/2019
Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games
Learning in a multi-agent system is challenging because agents are simul...
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09/12/2018
Bayes-ToMoP: A Fast Detection and Best Response Algorithm Towards Sophisticated Opponents
Multiagent algorithms often aim to accurately predict the behaviors of o...
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06/11/2018
Context-Aware Policy Reuse
Transfer learning can greatly speed up reinforcement learning for a new ...
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09/24/2017
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning
Transfer learning significantly accelerates the reinforcement learning p...
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03/06/2017