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Found 6 papers, 2 papers with code
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
RLHF and IIA: Perverse Incentives
Existing algorithms for reinforcement learning from human feedback (RLHF) can incentivize responses at odds with preferences because they are based on models that assume independence of irrelevant alternatives (IIA).
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models
In this work, we adopt a novel perspective wherein a pre-trained language model is itself simultaneously a policy, reward function, and transition function.
Posterior Sampling for Continuing Environments
We develop an extension of posterior sampling for reinforcement learning (PSRL) that is suited for a continuing agent-environment interface and integrates naturally into agent designs that scale to complex environments.
Uniformly Conservative Exploration in Reinforcement Learning
A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes.
Distribution of Eigenvalues of Matrix Ensembles arising from Wigner and Palindromic Toeplitz Blocks
For definiteness we concentrate on the ensemble of palindromic real symmetric Toeplitz (PST) matrices and the ensemble of real symmetric matrices, whose limiting spectral measures are the Gaussian and semi-circular distributions, respectively; these were chosen as they are the two extreme cases in terms of moment calculations.
Probability 15A52 (primary), 60F99, 62H10 (secondary)
