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Found 12 papers, 4 papers with code
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Current results guaranteeing the existence of optimal policies in LfPF problems assume that both the preferences and transition dynamics are determined by a Markov Decision Process.
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes
Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning.
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization.
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms.
Riemannian Diffusion Models
In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation.
Extracting Weighted Automata for Approximate Minimization in Language Modelling
The objective is to obtain a weighted finite automaton (WFA) that fits within a given size constraint and which mimics the behaviour of the original model while minimizing some notion of distance between the black box and the extracted WFA.
MICo: Improved representations via sampling-based state similarity for Markov decision processes
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effective means of shaping the learnt representations of deep reinforcement learning agents.
A Study of Policy Gradient on a Class of Exactly Solvable Models
Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return.
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes.
Latent Variable Modelling with Hyperbolic Normalizing Flows
One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions.
Basis refinement strategies for linear value function approximation in MDPs
We provide a theoretical framework for analyzing basis function construction for linear value function approximation in Markov Decision Processes (MDPs).
Proceedings of the 11th workshop on Quantum Physics and Logic
The first QPL under the new name Quantum Physics and Logic was held in Reykjavik (2008), followed by Oxford (2009 and 2010), Nijmegen (2011), Brussels (2012) and Barcelona (2013).
