Search Results for author:
Found 9 papers, 1 papers with code
Goal-conditioned Offline Reinforcement Learning through State Space Partitioning
Offline reinforcement learning (RL) aims to infer sequential decision policies using only offline datasets.
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help
Multi-agent reinforcement learning (MARL) has achieved great progress in cooperative tasks in recent years.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control
By pretraining with non-expert demonstrations, PwD-MARL improves sample efficiency in the process of online MARL with a warm start.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Flocking control is a challenging problem, where multiple agents, such as drones or vehicles, need to reach a target position while maintaining the flock and avoiding collisions with obstacles and collisions among agents in the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning
In this paper, we explore the spatiotemporal structure of agents' decisions and consider the hierarchy of coordination from the perspective of multilevel emergence dynamics, based on which a novel approach, Learning to Advise and Learning from Advice (LALA), is proposed to improve MARL.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning
In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition
Analyzing human affect is vital for human-computer interaction systems.
Supervised Off-Policy Ranking
Inspired by the two observations, in this work, we study a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of target policies based on supervised learning by leveraging off-policy data and policies with known performance.
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Accelerating deep model training and inference is crucial in practice.
