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EE 277

Reinforcement Learning: Behaviors and Applications

  • Not Offered

3 units

Letter or Credit/No Credit

Reinforcement learning addresses the design of agents that improve decisions while operating within complex and uncertain environments. This course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include environment models, planning, abstraction, prediction, credit assignment, exploration, and generalization. Motivating examples will be drawn from web services, control, finance, and communications. Prerequisites: EE278 or MS&E 221, EE104 or CS229, CS106A.

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