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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
These are some projects I did before graduating from BITS-Pilani in 2015. Read more
TL;DR: Deep RL to optimize constrained resource allocation at city scale. Good results on realistic datasets.
TL;DR: Deep RL to control hyperparameters of anytime algorithms at runtime to optimize quality of the final solution. Good results on Anytime A* search algorithm.
TL;DR: Randomized Weighted A* tunes the weight in Anytime Weighted A* randomly at runtime and outperforms every static weighted baseline.
TL;DR: Meta-level deep RL to adapt the rollout-length in model-based RL non-myopically based on feedback from the learning process, such as accuracy of the model, learning progress and scarcity of samples.
Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning ICAPS 2022
TL;DR: Deep RL to determine optimal stopping point and hyperparameters of anytime algorithms at runtime to optimize utility of the final solution. Good results on Anytime A* search algorithm and RRT* motion planning algorithm.
Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning IROS 2022
Nashed, S.B., Svegliato, J., Bhatia, A., Russell S., Zilberstein, S. (2022). In IEEE/RSJ International Conference on Intelligent Robots and Systems. PDF
TL;DR: Incorporating task-specific Q-value estimates as inputs to a meta-RL policy can lead to improved generalization and better performance on long horizon tasks.
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown! Read more
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. Read more
Undergraduate course, College of Information & Computer Sciences, University of Massachusetts Amherst