Search Results for author:
Found 12 papers, 2 papers with code
ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments.
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
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.
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 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
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.
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
