
Scalable agent alignment via reward modeling: a research direction
One obstacle to applying reinforcement learning algorithms to realworld...
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Learning Human Objectives by Evaluating Hypothetical Behavior
We seek to align agent behavior with a user's objectives in a reinforcem...
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Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcementlearning agents can learn ...
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Reward learning from human preferences and demonstrations in Atari
To solve complex realworld problems with reinforcement learning, we can...
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Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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Universal Reinforcement Learning Algorithms: Survey and Experiments
Many stateoftheart reinforcement learning (RL) algorithms typically a...
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Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact useful...
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Nonparametric General Reinforcement Learning
Reinforcement learning (RL) problems are often phrased in terms of Marko...
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A Formal Solution to the Grain of Truth Problem
A Bayesian agent acting in a multiagent environment learns to predict t...
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Exploration Potential
We introduce exploration potential, a quantity that measures how much a ...
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On the Computability of AIXI
How could we solve the machine learning and the artificial intelligence ...
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Bad Universal Priors and Notions of Optimality
A big open question of algorithmic information theory is the choice of t...
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A Definition of Happiness for Reinforcement Learning Agents
What is happiness for reinforcement learning agents? We seek a formal de...
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Loss Bounds and Time Complexity for Speed Priors
This paper establishes for the first time the predictive performance of ...
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Thompson Sampling is Asymptotically Optimal in General Environments
We discuss a variant of Thompson sampling for nonparametric reinforcemen...
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AI Safety Gridworlds
We present a suite of reinforcement learning environments illustrating v...
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