Improving Generalization for Temporal Difference Learning: The Successor Representation
نویسندگان
چکیده
منابع مشابه
Improving Generalization for Temporal Difference Learning: The Successor Representation
Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. Appropriate generalization between states is determined by how similar their successors are, and representations should follow suit. This paper shows how TD machinery can be used to learn such representations, and illustrates, using...
متن کاملImproving Generalisation for Temporal Difference Learning: The Successor Representation
Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. Appropriate generalisation between states is determined by how similar their successors are, and representations should follow suit. This paper shows howTDmachinery can be used to learn such representations, and illustrates, using a...
متن کاملRunning head: SUCCESSOR REPRESENTATION and TEMPORAL CONTEXT The Successor Representation and Temporal Context
The successor representation was introduced into reinforcement learning by Dayan (1993) as a means of facilitating generalization between states with similar successors. Although reinforcement learning in general has been used extensively as a model of psychological and neural processes, the psychological validity of the successor representation has yet to be explored. An interesting possibilit...
متن کاملThe Successor Representation and Temporal Context
The successor representation was introduced into reinforcement learning by Dayan ( 1993 ) as a means of facilitating generalization between states with similar successors. Although reinforcement learning in general has been used extensively as a model of psychological and neural processes, the psychological validity of the successor representation has yet to be explored. An interesting possibil...
متن کاملThe successor representation in human reinforcement learning
Theories of reward learning in neuroscience have focused on two families of algorithms, thought to capture deliberative vs. habitual choice. “Model-based” algorithms compute the value of candidate actions from scratch, whereas “model-free” algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor r...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 1993
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco.1993.5.4.613