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Found 6 papers, 3 papers with code
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
In this paper, we tackle the fundamental dilemma of offline-to-online fine-tuning: if the agent remains pessimistic, it may fail to learn a better policy, while if it becomes optimistic directly, performance may suffer from a sudden drop.
Efficient Multi-agent Reinforcement Learning by Planning
Extensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in terms of both sample and computational efficiency.
Computational Efficiency
Model-based Reinforcement Learning
+2
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
Generalizable Episodic Memory for Deep Reinforcement Learning
Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning.
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.
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.
