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Composing egocentric and allocentric maps for flexible navigation
Daniel Shani1,2,3, Peter Dayan1,2
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
This study introduces egocentric successor representations (SRs) combined with allocentric SRs for navigation. This integration enhances an agent's ability to learn policies and adapt to new environments efficiently.
Area of Science:
- Cognitive Science
- Neuroscience
- Artificial Intelligence
Background:
- Egocentric representations are traditionally limited to simple behaviors.
- Egocentric representations may exist in the hippocampal formation.
- Modern navigation methods have not fully integrated egocentric representations.
Purpose of the Study:
- Investigate egocentric successor representations (SRs).
- Combine egocentric SRs with allocentric representations for navigation.
- Develop a reinforcement learning agent for complex 2D environments.
Main Methods:
- Built a reinforcement learning agent.
- Combined egocentric and allocentric SRs.
- Trained the agent in complex 2D environments.
Main Results:
- The agent learned generalizable egocentric and allocentric value functions.
- Additive composition of representations enabled efficient policy learning.
- The agent adapted quickly to new environments.
Conclusions:
- Egocentric relational structure is beneficial for navigation.
- Cognitive maps can be composed from multiple simple maps.
- This approach offers a new perspective on cognitive map composition.
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