Results 111 to 120 of about 544,487 (316)
Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics [PDF]
arXivNaively trained Deep Reinforcement Learning agents may fail to satisfy vital safety constraints. To avoid costly retraining, we may desire to repair a previously trained reinforcement learning agent to obviate unsafe behaviour. We devise a counterexample-guided repair algorithm for repairing reinforcement learning systems leveraging safety critics. The
arxiv
Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning [PDF]
arXiv, 2017 This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization ...
arxiv
Learning with prolonged delay of reinforcement [PDF]
, 1966 John García +2 more
openalex +1 more source
Computational Modeling of Reticular Materials: The Past, the Present, and the Future
Advanced Materials, EarlyView.Reticular materials are advanced materials with applications in emerging technologies. A thorough understanding of material properties at operating conditions is critical to accelerate the deployment at an industrial scale. Herein, the status of computational modeling of reticular materials is reviewed, supplemented with topical examples highlighting ...
Wim Temmerman +3 more
wiley +1 more source
Reinforcement Learning to Rank [PDF]
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019 Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent.
openaire +3 more sources
Conventional and reversed partial reinforcement effects in selective learning [PDF]
, 1964 William B. Pavlik, William F. Reynolds
openalex +1 more source
A Bio‐Inspired Perspective on Materials Sustainability
Advanced Materials, EarlyView.This perspective discusses natural materials as inspiration for sustainable engineering designs and the processing of materials. First, circularity, longevity, parsimony, and activity are presented as essential material paradigms. The perspective then uses many examples of natural and technical materials to introduce principles such as oligo ...
Wolfgang Wagermaier +2 more
wiley +1 more source
Interpretable Reinforcement Learning with Ensemble Methods [PDF]
arXiv, 2018 We propose to use boosted regression trees as a way to compute human-interpretable solutions to reinforcement learning problems. Boosting combines several regression trees to improve their accuracy without significantly reducing their inherent interpretability.
arxiv
Effects of incidental training and reinforcement on mixed schema learning [PDF]
, 1968 Ed M. Edmonds, Marvin R. Mueller
openalex +1 more source
Advanced Materials, EarlyView.The SciAgents AI model drives hypothesis generation by harnessing multi‐agent graph reasoning, extracting insights from knowledge graphs constructed from scientific papers. Each agent plays a specific role: the Ontologist defines concepts, the Scientists draft and refine proposals, and the Critic reviews.
Alireza Ghafarollahi, Markus J. Buehler
wiley +1 more source