The taxicab problem domain is well known in the area of reinforcement learning. Simply put, a taxicab driver is tasked with the problem of picking up a passenger and delivering him to his destination in as few steps as possible. Typically, the taxi is constrained by a limit on the amount of fuel that can be carried.
The canonical taxicab problem is a 5x5 gridworld. There are four cells which serve as possible starting locations and possible destinations for the passenger. There is a...
3 results in 0.0026 seconds.
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Taxi (Hierarchical Reinforcement Learning)
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Created by:
Soar
in AgentsTaxi (Hierarchical Reinforcement Learning)
This agent simulates an omniscient taxi driver that uses reinforcement learning and hierarchical task decomposition to improve its performance over runs.
Soar capabilities- Hierarchical task decomposition
- Reinforcement learning
- The agent is included in the Taxi download.
- None.
- Bloch, M.K. Hierarchical Reinforcement Learning in the Taxicab Domain. (Report No. CCA-TR-2009-02). Ann Arbor, MI: Center
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Created by:
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Taxi (Reinforcement Learning)
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Created by:
in AgentsTaxi (Reinforcement Learning)
This agent simulates an omniscient taxi driver that uses reinforcement learning to improve its performance over runs.
Soar capabilities- Reinforcement Learning
- The agent is included in the Taxi download.
- None.
- Bloch, M.K. Hierarchical Reinforcement Learning in the Taxicab Domain. (Report No. CCA-TR-2009-02). Ann Arbor, MI: Center for Cognitive Architecture, University of Michigan. (2009)
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Created by: