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Metrics for Finite Markov Decision Processes
We present metrics for measuring the similarity of states in a finite Ma...
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Unifying task specification in reinforcement learning
Reinforcement learning tasks are typically specified as Markov decision ...
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A Taxonomy of Similarity Metrics for Markov Decision Processes
Although the notion of task similarity is potentially interesting in a w...
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Reinforcement Learning under Threats
In several reinforcement learning (RL) scenarios, mainly in security set...
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Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically a...
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Scaling up budgeted reinforcement learning
Can we learn a control policy able to adapt its behaviour in real time s...
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Graying the black box: Understanding DQNs
In recent years there is a growing interest in using deep representation...
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Metrics and continuity in reinforcement learning
In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage state similarity (whether explicitly or implicitly) to build models that can generalize well from a limited set of samples. The notion of state similarity used, and the neighbourhoods and topologies they induce, is thus of crucial importance, as it will directly affect the performance of the algorithms. Indeed, a number of recent works introduce algorithms assuming the existence of "well-behaved" neighbourhoods, but leave the full specification of such topologies for future work. In this paper we introduce a unified formalism for defining these topologies through the lens of metrics. We establish a hierarchy amongst these metrics and demonstrate their theoretical implications on the Markov Decision Process specifying the reinforcement learning problem. We complement our theoretical results with empirical evaluations showcasing the differences between the metrics considered.
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