Convex Q|Learning

Convex Q-Learning, Part 1: Deterministic Optimal Control - arXiv

The new class of convex Q-learning algorithms is introduced based on the convex relaxation of the Bellman equation. Convergence is established under general ...

Convex Q Learning in a Stochastic Environment: Extended Version

Title:Convex Q Learning in a Stochastic Environment: Extended Version ... Abstract:The paper introduces the first formulation of convex Q-learning ...

Convex Analytic Theory for Convex Q-Learning - IEEE Xplore

The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne's LP or a version of logistic Q-learning, but has similar structure that ...

Convex Q-Learning - NREL

Convex Q-learning. ArXiv e-prints:2008.03559, 2020. Background: [3] P. G. Mehta and S. P. Meyn. Q-learning and Pontryagin's minimum principle.

Convex Q-Learning - IEEE Xplore

(i) A new class of convex Q-learning algorithms is intro- duced based on a convex relaxation of the Bellman equation. Convergence is established under general ...

Reinforcement learning through convex programming - ScienceDirect

This work builds on model-free RL, namely Q-learning, to determine optimal control policies for nonlinear, complex biochemical processes.

An Introduction to Convex Learning Problems | by Helene - Medium

A convex learning problem is a problem whose hypothesis class is a convex set, and whose loss function is a convex function.

Lecture 9 (Convex Learning Problems)

In general, a convex learning problem is a problem where the hypothesis class is a convex set, and the loss function is a convex function for each example.

Convex Reinforcement Learning in Finite Trials

Keywords: Reinforcement Learning, Convex Reinforcement Learning, General Utilities, ... q(x) . We further define the Kullback-Leibler (KL) divergence between p ...

AM Seminar: Convex Q-Learning: Theory and Application

We propose a new set of reinforcement learning algorithms, called convex Q learning, based on the convex relaxation of the Bellman equation.

Convex Analytic Theory for Convex Q-Learning - Illinois Experts

The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne's LP or a version of logistic Q-learning, but has similar structure that ...

Convex Reinforcement Learning in Finite Trials

Convex Reinforcement Learning in Finite Trials. Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli; 24(250):1−42, 2023.

Convex theory and RL : r/reinforcementlearning - Reddit

There is a subject based on safety certification for reinforcement learning. You can check some papers by Claire Tomlin and Melanie Zeilinger.

Convex Q Learning in a Stochastic Environment: Extended Version

Request PDF | Convex Q Learning in a Stochastic Environment: Extended Version | The paper introduces the first formulation of convex Q-learning for Markov ...

Convex Multi-Task Feature Learning - TTIC

If X is an n× q real matrix, range(X) denotes the set. {x ∈ Rn : x = Xz, for some z ∈ Rq}. Moreover, null(X) denotes the set. {x ∈ Rq : Xx = 0}. We let Od ...

Sparse Reinforcement Learning via Convex Optimization

Sparse Reinforcement Learning via Convex OptimizationZhiwei Qin, Weichang Li, Firdaus JanoosWe propose two new algorithms for the sparse reinforcem...

Reinforcement Learning with Convex Constraints

Abstract. In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior ...

Sufficient Exploration for Convex Q-learning | Request PDF

The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne's LP or a version of logistic Q-learning, but has similar structure that ...

Convex Learning of Multiple Tasks and their Structure

In this case, the objective function in (Q) is separately convex in C and A but not jointly convex. Hence, block coordinate methods, which are often used in ...

Machine learning theory - Convex learning problems

Let (H,Z,`) be a convex-Lipschitz-bounded learning problem with parameters ρ,B. For any training set size m, let λ = q. 2ρ2. B2m . Then,the ...