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10 papers · 1 filter
Quantifying stability of non-power-seeking in artificial agents
Evan Ryan Gunter, Yevgeny Liokumovich, Victoria Krakovna
We investigate the question: if an AI agent is known to be safe in one setting, is it also safe in a new setting similar to the first? This is a core question of AI alignment--we t…
Policy-Guided Heuristic Search with Guarantees
Laurent Orseau, Levi H. S. Lelis
The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on th…
Relative Variational Intrinsic Control
Kate Baumli, David Warde-Farley, Steven Hansen +1
In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment. Existing skill learning m…
On the role of planning in model-based deep reinforcement learning
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani +7
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learn…
The Advantage Regret-Matching Actor-Critic
Audrūnas Gruslys, Marc Lanctot, Rémi Munos +10
Regret minimization has played a key role in online learning, equilibrium computation in games, and reinforcement learning (RL). In this paper, we describe a general model-free RL…
Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park, Ryan A. Rossi, Zheng Wen +2
Linear quadratic regulator (LQR) is one of the most popular frameworks to tackle continuous Markov decision process tasks. With its fundamental theory and tractable optimal policy,…