1 citations · 1 across the 6 of their papers we have counts for
7 papers
Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery
Akhil Bagaria, Anita De Mello Koch, George Konidaris
Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target all aspects of the state sim…
Proximal Residual Value Functions for Consistent Planning and Real-Time Execution
Harrison Waldon, Carson Eisenach, Akhil Bagaria +4
We study two-timescale decision systems in which a planning layer periodically supplies a continuation-value function to a real-time optimizer that allocates arriving resources, wi…
Structure-Informed Deep Reinforcement Learning for Inventory Management
Alvaro Maggiar, Sohrab Andaz, Akhil Bagaria +4
This paper investigates the application of Deep Reinforcement Learning (DRL) to classical inventory management problems, with a focus on practical implementation considerations. We…
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
Sam Lobel, Akhil Bagaria, George Konidaris
We propose a new method for count-based exploration in high-dimensional state spaces. Unlike previous work which relies on density models, we show that counts can be derived by ave…
Scaling Goal-based Exploration via Pruning Proto-goals
Akhil Bagaria, Ray Jiang, Ramana Kumar +1
One of the gnarliest challenges in reinforcement learning (RL) is exploration that scales to vast domains, where novelty-, or coverage-seeking behaviour falls short. Goal-directed,…
Sphynx: A Shared Instruction Cache Exporatory Study
Dong-hyeon Park, Akhil Bagaria, Fabiha Hannan +2
The Sphynx project was an exploratory study to discover what might be done to improve the heavy replication of in- structions in independent instruction caches for a massively para…