38 citations · 38 across the 3 of their papers we have counts for
6 papers
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao +2
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a u…
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
Silviu Pitis, Harris Chan, Kiarash Jamali +1
Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When de…
Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes
Silviu Pitis, Michael R. Zhang
How should one combine noisy information from diverse sources to make an inference about an objective ground truth? This frequently recurring, normative question lies at the core o…
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning
Kristopher De Asis, Alan Chan, Silviu Pitis +2
We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a $\textit{fixed…
Source Traces for Temporal Difference Learning
Silviu Pitis
This paper motivates and develops source traces for temporal difference (TD) learning in the tabular setting. Source traces are like eligibility traces, but model potential histori…
Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach
Silviu Pitis
Reinforcement learning (RL) agents have traditionally been tasked with maximizing the value function of a Markov decision process (MDP), either in continuous settings, with fixed d…