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Found 3 papers, 2 papers with code
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
This result makes it possible to extend the guarantees of existing MBRL algorithms to the setting with abstraction.
Model-based Reinforcement Learning
reinforcement-learning
+1
Loss Bounds for Approximate Influence-Based Abstraction
Sequential decision making techniques hold great promise to improve the performance of many real-world systems, but computational complexity hampers their principled application.
Influence-aware Memory Architectures for Deep Reinforcement Learning
Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally.
