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The course provides an introduction to the field of reinforcement learning (RL) concerned with how a software agent learns to behave in an environment with the aim of maximizing some reward. Students will be exposed to both theoretical and practical aspects of reinforcement learning, including exploration and generalization, the definition of state space, action space, dynamics, and rewards, on-policy and off-policy learning, value iteration and policy iteration RL, and methods and algorithms for RL.
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