1 code implementation • 10 Nov 2023 • Jared Markowitz, Edward W. Staley
To facilitate efficient learning, policy gradient approaches to deep reinforcement learning (RL) are typically paired with variance reduction measures and strategies for making large but safe policy changes based on a batch of experiences.
no code implementations • 8 Dec 2017 • Jared Markowitz, Aurora C. Schmidt, Philippe M. Burlina, I-Jeng Wang
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation.
no code implementations • 7 Nov 2018 • Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens
This paper provides a complexity analysis for the game of reconnaissance blind chess (RBC), a recently-introduced variant of chess where each player does not know the positions of the opponent's pieces a priori but may reveal a subset of them through chosen, private sensing actions.
no code implementations • 11 Dec 2020 • Nathan Drenkow, Philippe Burlina, Neil Fendley, Onyekachi Odoemene, Jared Markowitz
We interpret both detection problems through a probabilistic, Bayesian lens, whereby the objectness maps produced by our method serve as priors in a maximum-a-posteriori approach to the detection step.
no code implementations • 22 Dec 2020 • Kapil Katyal, Yuxiang Gao, Jared Markowitz, Sara Pohland, Corban Rivera, I-Jeng Wang, Chien-Ming Huang
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments.
no code implementations • 29 Sep 2021 • Jared Markowitz, Ryan Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Standard deep reinforcement learning (DRL) agents aim to maximize expected reward, considering collected experiences equally in formulating a policy.
no code implementations • 1 Dec 2021 • Edward W. Staley, Chace Ashcraft, Benjamin Stoler, Jared Markowitz, Gautam Vallabha, Christopher Ratto, Kapil D. Katyal
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time.
no code implementations • 2 May 2022 • Edward W. Staley, Jared Markowitz
After training, the layer can be arbitrarily reduced in width to exchange performance for narrowness.
1 code implementation • 19 Aug 2022 • Jared Markowitz, Ryan W. Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation.
no code implementations • 30 Nov 2023 • Jared Markowitz, Jesse Silverberg, Gary Collins
This setting is common in real-world applications, and may be addressed with or without constraints on the cost terms.