Graduate Artificial Intelligence 15 - 780 Homework # 3 : MDPs , Q - Learning , & POMDPs \ Out on
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Memory Based Learning in Partially Observable Markov Decision Processes with Variable Length Histories
In the field of artificial intelligence, many are interested in finding new algorithms that enable an agent to act intelligently in a world. Planning how to act in a stochastic world is a major problem in the field. An intelligent agent must usually rely on an imperfect model of the world to plan its actions. To improve the model used, the agent can learn a better model through experience; this...
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Reinforcement learning RL algorithms pro vide a sound theoretical basis for building learning control architectures for embedded agents Unfortunately all of the theory and much of the practice see Barto et al for an exception of RL is limited to Marko vian decision processes MDPs Many real world decision tasks however are inherently non Markovian i e the state of the environ ment is only incomp...
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General-purpose, intelligent, learning agents cycle through sequences of observations, actions, and rewards that are complex, uncertain, unknown, and non-Markovian. On the other hand, reinforcement learning is well-developed for small finite state Markov decision processes (MDPs). Up to now, extracting the right state representations out of bare observations, that is, reducing the general agent...
متن کامل15381: ARTIFICIAL INTELLIGENCE (FALL 2014) Homework 2: Planning, MDPs, and Reinforcement Learning (Solutions)
Let S be a set of disjoint obstacles (simple polygons) in the plane. We use n to denote the total number of their edges. Assume that we have a point robot moving on the plane and that it can “walk” on the edges of the obstacles (that is, we treat the obstacles as open sets). The robot starts from pstart position and has to get to pgoal position using the shortest collision-free path. In class, ...
متن کاملFree-energy-based reinforcement learning in a partially observable environment
Free-energy-based reinforcement learning (FERL) can handle Markov decision processes (MDPs) with high-dimensional state spaces by approximating the state-action value function with the negative equilibrium free energy of a restricted Boltzmann machine (RBM). In this study, we extend the FERL framework to handle partially observable MDPs (POMDPs) by incorporating a recurrent neural network that ...
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تاریخ انتشار 2013