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Introduction Reinforcement Learning (RL) relies on Markov Decision Processes (MDP) as its cornerstone, a general framework under which vastly different problems can be casted. There is a clear separation in the class of MDPs between the finite discrete action setup, where an agent faces a finite number of possible actions, and the continuous action setup, where an agent faces an infinite number of actions.
Abstract: Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans.
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