37 citations · 56 across the 13 of their papers we have counts for
6 papers · 1 filter
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil +3
We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, whi…
A Markov Decision Process Approach to Active Meta Learning
Bingjia Wang, Alec Koppel, Vikram Krishnamurthy
In supervised learning, we fit a single statistical model to a given data set, assuming that the data is associated with a singular task, which yields well-tuned models for specifi…
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
Junyu Zhang, Alec Koppel, Amrit Singh Bedi +2
In recent years, reinforcement learning (RL) systems with general goals beyond a cumulative sum of rewards have gained traction, such as in constrained problems, exploration, and a…
Policy Gradient using Weak Derivatives for Reinforcement Learning
Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy
This paper considers policy search in continuous state-action reinforcement learning problems. Typically, one computes search directions using a classic expression for the policy g…
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we prop…
Doubly Random Parallel Stochastic Methods for Large Scale Learning
Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we prop…