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pro vyhledávání: '"Villaflor, Adam"'
Autor:
Villaflor, Adam, Yang, Brian, Su, Huangyuan, Fragkiadaki, Katerina, Dolan, John, Schneider, Jeff
Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Althou
Externí odkaz:
http://arxiv.org/abs/2403.07232
Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem. Recent works based o
Externí odkaz:
http://arxiv.org/abs/2207.10295
The problem of offline reinforcement learning focuses on learning a good policy from a log of environment interactions. Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning
Externí odkaz:
http://arxiv.org/abs/2204.12026
Recently, autonomous driving has made substantial progress in addressing the most common traffic scenarios like intersection navigation and lane changing. However, most of these successes have been limited to scenarios with well-defined traffic rules
Externí odkaz:
http://arxiv.org/abs/2103.12070
A general-purpose intelligent robot must be able to learn autonomously and be able to accomplish multiple tasks in order to be deployed in the real world. However, standard reinforcement learning approaches learn separate task-specific policies and a
Externí odkaz:
http://arxiv.org/abs/1810.07167
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to na
Externí odkaz:
http://arxiv.org/abs/1709.10489
Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can
Externí odkaz:
http://arxiv.org/abs/1702.01182