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Learning efficient reactive behavioral sequences from basic reflexes in a goal-directed autonomous robot
| Content Provider | Semantic Scholar |
|---|---|
| Author | Millán, José Del R. |
| Copyright Year | 1994 |
| Abstract | This paper describes a reinforcement connectionist learning mechanism that allows a goaldirected autonomous mobile robot to adapt to an unknown indoor environment in a few trials. As a result, the robot learns efficient reactive behavioral sequences. In addition to quick convergence, the learning mechanism satisfies two further requirements. First, the robot improves its performance incrementally and permanently. Second, the robot is operational from the very beginning, what reduces the risk of catastrophic failures (collisions). The learning mechanism is based on three main ideas. The first idea applies when the neural network does not react properly to the current situation: a fixed set of basic reflexes suggests where to search for a suitable action. The second is to use a resource-allocating procedure to build automatically a modular network with a suitable structure and size. Each module codifies a similar set of reaction rules. The third idea consists on concentrating the exploration of the action space around the best actions currently known. The paper also reports experimental results obtained with a real mobile robot that demonstrate the feasibility of our approach. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www8.cs.umu.se/research/ifor/dl/SEQUENCE%20LEARINIG/Learning%20Effcient%20Reactive%20Behavioral%20Sequences.pdf |
| Alternate Webpage(s) | http://www.funet.fi/pub/sci/neural/neuroprose/millan.sab94.ps.Z |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
