Search Results for author:
Found 19 papers, 11 papers with code
ChatMotion: A Multimodal Multi-Agent for Human Motion Analysis
Advancements in Multimodal Large Language Models (MLLMs) have improved human motion understanding.
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
We find a return-based uncertainty quantification for IDAQ that performs effectively.
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework
Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
Self-Organized Polynomial-Time Coordination Graphs
However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values.
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
Offline Reinforcement Learning with Reverse Model-based Imagination
These reverse imaginations provide informed data augmentation for model-free policy learning and enable conservative generalization beyond the offline dataset.
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates
During long-time observations, agents can build \textit{awareness} for teammates to alleviate the problem of partial observability.
Active Hierarchical Exploration with Stable Subgoal Representation Learning
Although GCHRL possesses superior exploration ability by decomposing tasks via subgoals, existing GCHRL methods struggle in temporally extended tasks with sparse external rewards, since the high-level policy learning relies on external rewards.
Intrinsically Guided Exploration in Meta Reinforcement Learning
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
Learning Subgoal Representations with Slow Dynamics
In goal-conditioned Hierarchical Reinforcement Learning (HRL), a high-level policy periodically sets subgoals for a low-level policy, and the low-level policy is trained to reach those subgoals.
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning
Value decomposition is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings.
QPLEX: Duplex Dueling Multi-Agent Q-Learning
This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the representational capacity of value functions.
Influence-Based Multi-Agent Exploration
We present two exploration methods: exploration via information-theoretic influence (EITI) and exploration via decision-theoretic influence (EDTI), by exploiting the role of interaction in coordinated behaviors of agents.
Learning Nearly Decomposable Value Functions Via Communication Minimization
Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems.
Object-Oriented Dynamics Learning through Multi-Level Abstraction
We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability.
