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Policy search with rare significant events: Choosing the right partner to cooperate with [PDF]
PLoS ONE, 2022 This paper focuses on a class of reinforcement learning problems where significant events are rare and limited to a single positive reward per episode.
Paul Ecoffet +3 more
doaj +3 more sources
From Levin’s Universal Search to Policy-Guided Tree Search [PDF]
EntropyLevin’s universal search embodies a striking principle: when a solution is efficiently verifiable, one can allocate search effort across candidate procedures according to a prior and obtain performance competitive (up to constant) with the best procedure
Ming Li
doaj +2 more sources
Bio-based and Applied Economics, 2019 Both the revised EU Bioeconomy strategy and the proposals for the Common Agricultural Policy (CAP) 2021-2027 were released in 2018. This paper explores the connection between these two policy areas, the needs for economic and policy research and the way ...
Davide Viaggi
doaj +4 more sources
A Markov chain Monte Carlo algorithm for Bayesian policy search
Systems Science & Control Engineering, 2018 Policy search algorithms have facilitated application of Reinforcement Learning (RL) to dynamic systems, such as control of robots. Many policy search algorithms are based on the policy gradient, and thus may suffer from slow convergence or local optima ...
Vahid Tavakol Aghaei +2 more
doaj +2 more sources
Compatible natural gradient policy search [PDF]
Machine Learning, 2019 Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a standard exponential policy ...
Joni Pajarinen +2 more
exaly +8 more sources
Geometric Reinforcement Learning for Robotic Manipulation
IEEE Access, 2023 Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment.
Naseem Alhousani +5 more
doaj +1 more source
Designing Lookahead Policies for Sequential Decision Problems in Transportation and Logistics
IEEE Open Journal of Intelligent Transportation Systems, 2022 There is a wide range of sequential decision problems in transportation and logistics that require dealing with uncertainty. There are four classes of policies that we can draw on for different types of decisions, but many problems in transportation and ...
Warren B. Powell
doaj +1 more source
A Survey on Policy Search for Robotics [PDF]
Foundations and Trends in Robotics, 2011 Policy search is a subfield in reinforcement learning which focuses onfinding good parameters for a given policy parametrization. It is wellsuited for robotics as it can cope with high-dimensional state and actionspaces, one of the main challenges in robot learning.
Deisenroth, M. P. +2 more
openaire +4 more sources
Generalized exploration in policy search [PDF]
Machine Learning, 2017 To learn control policies in unknown environments, learning agents need to explore by trying actions deemed suboptimal. In prior work, such exploration is performed by either perturbing the actions at each time-step independently, or by perturbing policy parameters over an entire episode.
Herke van Hoof +2 more
openaire +2 more sources
Accelerating Robot Trajectory Learning for Stochastic Tasks
IEEE Access, 2020 Learning from demonstration provides ways to transfer knowledge and skills from humans to robots. Models based solely on learning from demonstration often have very good generalization capabilities but are not completely accurate when adapting to new ...
Josip Vidakovic +4 more
doaj +1 more source