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Found 5 papers, 3 papers with code
Advantage-Aware Policy Optimization for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the Out-Of-Distribution (OOD) problem.
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents to adopt global cooperative information from each other during centralized training.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems.
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
