| 1. |
- Borga, Magnus, et al.
(author)
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A Binary Competition Tree for Reinforcement Learning
- 1994
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Reports (other academic/artistic)abstract
- A robust, general and computationally simple reinforcement learning system is presented. It uses a channel representation which is robust and continuous. The accumulated knowledge is represented as a reward prediction function in the outer product space of the input- and output channel vectors. Each computational unit generates an output simply by a vector-matrix multiplication and the response can therefore be calculated fast. The response and a prediction of the reward are calculated simultaneously by the same system, which makes TD-methods easy to implement if needed. Several units can cooperate to solve more complicated problems. A dynamic tree structure of linear units is grown in order to divide the knowledge space into a sufficiently number of regions in which the reward function can be properly described. The tree continuously tests split- and prune criteria in order to adapt its size to the complexity of the problem.
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| 2. |
- Borga, Magnus, et al.
(author)
-
A Survey of Current Techniques for Reinforcement Learning
- 1992
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Reports (other academic/artistic)abstract
- This survey considers response generating systems that improve their behaviour using reinforcement learning. The difference between unsupervised learning, supervised learning, and reinforcement learning is described. Two general problems concerning learning systems are presented; the credit assignment problem and the problem of perceptual aliasing. Notations and some general issues concerning reinforcement learning systems are presented. Reinforcement learning systems are further divided into two main classes; memory mapping and projective mapping systems. Each of these classes is described and some examples are presented. Some other approaches are mentioned that do not fit into the two main classes. Finally some issues not covered by the surveyed articles are discussed, and some comments on the subject are made.
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| 3. |
- Borga, Magnus
(author)
-
Hierarchical Reinforcement Learning
- 1993
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In: ICANN'93 eds S. Gielen and B. Kappen.
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Conference paper (peer-reviewed)abstract
- A hierarchical representation of the input-output transition function in a learning system is suggested. The choice of either representing the knowledge in a learning system as a discrete set of input-output pairs or as a continuous input-output transition function is discussed. The conclusion that both representations could be efficient, but at different levels of abstraction is made. The difference between strategies and actions is defined. An algorithm for using adaptive critic methods in a two-level reinforcement learning system is presented. Simulations of a one dimensional hierarchical reinforcement learning system is presented.
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