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Found 5 papers, 0 papers with code
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly.
LDM$^2$: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement
With the rapid development of large language models (LLMs), it is highly demanded that LLMs can be adopted to make decisions to enable the artificial general intelligence.
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising approach to generating subpolicies. However, the degradation problem is a challenge that existing methods can hardly deal with due to the lack of consideration of diversity or the employment of weak regularizers.
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs
In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI.
Spread-gram: A spreading-activation schema of network structural learning
Inspired by a cognitive model of human memory, we propose a network representation learning scheme.
