Incorporating dynamic context-awareness into autoregressive models can further enhance hierarchical reinforcement learning performance in environments with non-stationary dynamics.

Motivation

The current approach focuses on temporal abstractions to improve hierarchical decision-making, but it does not explicitly account for non-stationary environments where the dynamics change over time. Introducing dynamic context-awareness could allow models to adapt more effectively to such changes, potentially leading to more robust learning.

Proposed Method

Develop an extension of the current model that integrates a context-aware mechanism, such as a recurrent neural network, to dynamically adjust temporal abstractions based on environmental changes. Test the model in non-stationary environments, such as those with evolving reward structures or dynamic obstacles, and compare its performance to the baseline method.

Expected Contribution

This research would demonstrate the effectiveness of dynamic context-awareness in improving the adaptability and robustness of hierarchical reinforcement learning models to non-stationary environments.

Required Resources

Access to a variety of non-stationary environment simulators, computational resources for model training, and expertise in neural network design.

Source Paper

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning

View Paper Details →