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About this paper
Amir Samani, Richard S. Sutton
A generate-and-test framework is introduced for online and continual learning of agent state in partially observable reinforcement learning environments. This approach, utilizing specialized deep trace and imprinting generators, enabled agents to accurately make multi-step predictions and learn complex non-linear feature configurations directly from continuous data streams.
Key insights
- A generate-and-test paradigm provides a computationally efficient and robust alternative to gradient-based methods for online and continual learning of agent state in recurrent architectures.
- Specialized generators can effectively imbue an agent with capabilities for multi-step prediction across trace intervals and for discovering complex non-linear feature combinations from raw experience.
- Protecting features that serve as sources for other features is essential for maintaining the integrity of hierarchical memory structures within the dynamically evolving agent state.
Summary by alphaXiv.
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