Search Results for author:
Found 23 papers, 9 papers with code
Value-Aided Conditional Supervised Learning for Offline RL
Offline reinforcement learning (RL) has seen notable advancements through return-conditioned supervised learning (RCSL) and value-based methods, yet each approach comes with its own set of practical challenges.
Domain Adaptive Imitation Learning with Visual Observation
In this paper, we consider domain-adaptive imitation learning with visual observation, where an agent in a target domain learns to perform a task by observing expert demonstrations in a source domain.
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model.
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making
However, we discovered that the attention module of DT is not appropriate to capture the inherent local dependence pattern in trajectories of RL modeled as a Markov decision process.
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning
Handling the problem of scalability is one of the essential issues for multi-agent reinforcement learning (MARL) algorithms to be applied to real-world problems typically involving massively many agents.
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-agent reinforcement learning to enable multiple agents to learn coordinated behaviors by regularizing the accumulated return with the simultaneous mutual information between multi-agent actions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
This paper proposes a framework, named Quantile Constrained RL (QCRL), to constrain the quantile of the distribution of the cumulative sum cost that is a necessary and sufficient condition to satisfy the outage constraint.
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward.
Robust Imitation Learning against Variations in Environment Dynamics
In this paper, we propose a robust imitation learning (IL) framework that improves the robustness of IL when environment dynamics are perturbed.
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems.
Partially Observable Reinforcement Learning
reinforcement-learning
+1
A Max-Min Entropy Framework for Reinforcement Learning
In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum entropy RL in model-free sample-based learning.
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning
In this paper, we consider intrinsic reward generation for sparse-reward reinforcement learning based on model prediction errors.
Communication in Multi-Agent Reinforcement Learning: Intention Sharing
Communication is one of the core components for learning coordinated behavior in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability
A condition for the reward function of reinforcement learning (RL) for queue stability is derived.
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning
In this paper, we propose a maximum mutual information (MMI) framework for multi-agent reinforcement learning (MARL) to enable multiple agents to learn coordinated behaviors by regularizing the accumulated return with the mutual information between actions.
Multiagent Systems
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
In this paper, sample-aware policy entropy regularization is proposed to enhance the conventional policy entropy regularization for better exploration.
Cross-Domain Imitation Learning with a Dual Structure
In this paper, we consider cross-domain imitation learning (CDIL) in which an agent in a target domain learns a policy to perform well in the target domain by observing expert demonstrations in a source domain without accessing any reward function.
Population-Guided Parallel Policy Search for Reinforcement Learning
In the proposed scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collaboration with the guidance of the best policy information.
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning
In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models.
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
In importance sampling (IS)-based reinforcement learning algorithms such as Proximal Policy Optimization (PPO), IS weights are typically clipped to avoid large variance in learning.
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning
In this paper, we propose a new learning technique named message-dropout to improve the performance for multi-agent deep reinforcement learning under two application scenarios: 1) classical multi-agent reinforcement learning with direct message communication among agents and 2) centralized training with decentralized execution.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
In this paper, a new interactive parallel learning scheme is proposed to enhance the performance of off-policy continuous-action reinforcement learning.
AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control
In this paper, a new adaptive multi-batch experience replay scheme is proposed for proximal policy optimization (PPO) for continuous action control.
