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Found 3 papers, 1 papers with code
Model-based Offline Policy Optimization with Adversarial Network
Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization.
Online Attentive Kernel-Based Temporal Difference Learning
Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA.
Multi-Agent Game Abstraction via Graph Attention Neural Network
Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents.
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