Difference between revisions of "Avoiding in a corridor"
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The goal of this project is to make two robots go through a corridor at the same time, until they face each other. Having one of them find a free slot where he can wait until the other robot passes and the way is clear. For that effect we will use a Reinforcement Learning algorithm. | The goal of this project is to make two robots go through a corridor at the same time, until they face each other. Having one of them find a free slot where he can wait until the other robot passes and the way is clear. For that effect we will use a Reinforcement Learning algorithm. | ||
| − | Reinforcement learning algorithm | + | Reinforcement learning algorithm (Q-Learning) |
| + | |||
| + | Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. | ||
source code | source code | ||
pictures, videos | pictures, videos | ||
Revision as of 18:52, 18 June 2012
About project
The goal of this project is to make two robots go through a corridor at the same time, until they face each other. Having one of them find a free slot where he can wait until the other robot passes and the way is clear. For that effect we will use a Reinforcement Learning algorithm.
Reinforcement learning algorithm (Q-Learning)
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering.
source code
pictures, videos
