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https://deepai.org/publication/happy-mine-designing-a-mining-reward-function HaPPY-Mine: Designing a Mining Reward Function | DeepAI Mar 22, 2021 - 03/22/21 - In cryptocurrencies, the block reward is meant to serve as the incentive mechanism for miners to commit resources to create blocks... reward function happy mine designing mining https://www.desmos.com/calculator/vrckrgjr0q Bayesian subjective probability reward function | Desmos Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations, add sliders, animate graphs, and... subjective probability reward function bayesian desmos https://openreview.net/forum?id=gAP52Z2dar Inverse Preference Learning: Preference-based RL without a Reward Function | OpenReview Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these... preference learning reward function inverse based rl https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2013.00059/full Frontiers | The hypocretins and the reward function: what have we learned so far? A general consensus acknowledges that drug consumption (including alcohol, tobacco and illicit drugs) constitutes the leading cause of preventable death worl... reward function https://openreview.net/forum?id=mVt55ZQqfTl Combinatorial Pure Exploration with Bottleneck Reward Function | OpenReview In this paper, we study the Combinatorial Pure Exploration problem with the Bottleneck reward function (CPE-B) under the fixed-confidence (FC) and fixed-budget... reward function combinatorial pure exploration bottleneck https://openreview.net/forum?id=Hn21kZHiCK A general framework for reward function distances | OpenReview In reward learning, it is helpful to be able to measure distances between reward functions, for example to evaluate learned reward models. Using simple metrics... general framework reward function distances openreview https://openreview.net/forum?id=VwUTz2pOnD Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist... Reinforcement Learning (RL) utilizing kernel ridge regression to predict the expected value function represents a powerful method with great representational... kernel based function approximation reinforcement learning https://openreview.net/forum?id=NvaZn3uwzJ&referrer=%5Bthe%20profile%20of%20Jason%20D.%20Lee%5D(%2Fprofile%3Fid%3D~Jason_D._Lee1) Deployment Efficient Reward-Free Exploration with Linear Function Approximation | OpenReview We study deployment efficient reward-free exploration with linear function approximation, where the goal is to explore a linear Markov Decision Process (MDP)... linear function deployment efficient reward free https://openreview.net/forum?id=fq1wNrC2ai Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function... We study infinite-horizon average-reward Markov decision processes (AMDPs) in the context of general function approximation. Specifically, we propose a novel... infinite horizon