Introduction to Deep Reinforcement Learning and Control

Introduction to deep Reinforcement Learning and ControlDeep Reinforcement Learning and ControlKaterina Fragkiadaki Carnegie MellonSchool of Computer ScienceLecture 1, CMU 10703 Logistics 3 assignments and a project Russ will announce those in the next lecture!Goal of the CourseHow to build agents that learn behaviors in a dynamic world?Behavior: a sequence of actions with a particular goalas opposed to agents that execute preprogrammed behavior in a static are ImportantThe brain evolved, not to think or feel, but to Control Wolpert, nice TED talkBehaviors are ImportantThe brain evolved, not to think or feel, but to Control Wolpert, nice TED talkSea squirts digest their own brain when they decide not to move anymoreBehaviors are ImportantThe brain evolved, not to think or feel, but to Control Wolpert, nice TED talkLearning behaviors that adapt to a changing environment is considered the hallmark of human intelligence (though definitions of intelligence are not easy) Learning BehaviorsLearning to map sequences of observations to actionsLearning BehaviorsLearning to map sequences of observations to actions, for a particular goalgoalgtSupervisionWhat supervision does an agent need to learn purposeful behaviors in dynamic environments?

Introduction to Deep Reinforcement Learning and Control Deep Reinforcement Learning and Control Katerina Fragkiadaki Carnegie Mellon School of Computer Science Lecture 1, CMU 10703. Logistics • 3 assignments and a project • Russ will announce those in the next lecture!

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  Introduction, Control, Learning, Deep, Reinforcement, Introduction to deep reinforcement learning and control, Introduction to deep reinforcement learning and control deep reinforcement learning and control

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