Inverse Reinforcement Learning
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Social LSTM: Human Trajectory Prediction in Crowded Spaces
cvgl.stanford.edual. in [32] use Inverse Reinforcement Learning to predict human paths in static scenes. They infer walkable paths in a scene by modeling human-space interactions. Walker et al. in [68] predict the behavior of generic agents (e.g., a ve-hicle) in a visual scene given a large collection of videos. Ziebart et al. [78,23] presented a planning based ...
Soft Actor-Critic: Off-Policy Maximum Entropy Deep ...
arxiv.orgMaximum entropy reinforcement learning optimizes poli-cies to maximize both the expected return and the ex-pected entropy of the policy. This framework has been used in many contexts, from inverse reinforcement learn-ing (Ziebart et al.,2008) to optimal control (Todorov,2008; Toussaint,2009;Rawlik et al.,2012). In guided policy
Chapter 2 Resource Masters - Math Problem Solving
jaeproblemsolving.weebly.com©Glencoe/McGraw-Hill iv Glencoe Geometry Teacher’s Guide to Using the Chapter 2 Resource Masters The Fast FileChapter Resource system allows you to conveniently file the resources you use most often. The Chapter 2 Resource Mastersincludes the core materials needed for Chapter 2. These materials include worksheets, extensions, and assessment options.