Shimkin 4 Reinforcement Learning – Basic Algorithms
نویسنده
چکیده
Our agent usually has only partial knowledge of its environment, and therefore will use some form of learning scheme, based on the observed signals. To start with, the agent needs to use some parametric model of the environment. We shall use the model of a stationary MDP, with given state space and actions space. However, the state transition matrix P = (p(s′|s, a)) and the immediate reward function r = (r(s, a, s′)) may not be given. We shall further assume the the observed signal is indeed the state of the dynamic proceed (fully observed MDP), and that the reward signal is the immediate reward rt, with mean r(st, at).
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