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pro vyhledávání: '"Morere, Philippe"'
State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem is to learn control policies by imitating a set of expert demonstration
Externí odkaz:
http://arxiv.org/abs/2106.09203
Autor:
Lai, Tin, Morere, Philippe
We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills which are each specialised to perform a single task well. Skills are
Externí odkaz:
http://arxiv.org/abs/2010.13033
Autor:
Morere, Philippe, Ramos, Fabio
Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy would typically fail. However, intrinsic exploration is generally handled
Externí odkaz:
http://arxiv.org/abs/2004.02380
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in the sparse reward case, where they can do no better than to explore in a
Externí odkaz:
http://arxiv.org/abs/2001.06940
Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness. However, most schemes are often inefficient as the samples
Externí odkaz:
http://arxiv.org/abs/1909.03452
Publikováno v:
IEEE Robotics and Automation Letters (2019)
We present a framework for learning to plan hierarchically in domains with unknown dynamics. We enhance planning performance by exploiting problem structure in several ways: (i) We simplify the search over plans by leveraging knowledge of skill objec
Externí odkaz:
http://arxiv.org/abs/1906.07371
Bayesian Optimisation has gained much popularity lately, as a global optimisation technique for functions that are expensive to evaluate or unknown a priori. While classical BO focuses on where to gather an observation next, it does not take into acc
Externí odkaz:
http://arxiv.org/abs/1703.04211
Akademický článek
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Autor:
Morere, Philippe
This thesis addresses the problem of achieving efficient non-myopic decision making by explicitly balancing exploration and exploitation. Decision making, both in planning and reinforcement learning (RL), enables agents or robots to complete tasks by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______293::1edcbec4f971b08660ed6c94120b9d34
https://hdl.handle.net/2123/21230
https://hdl.handle.net/2123/21230
Autor:
Chiroque, Luis F., Cordobés de la Calle, Héctor, Fernández Anta, Antonio|||0000-0001-6501-2377, García, Rafael, Morere, Philippe, Ornella, Lorenzo, Pérez, Fernando, Santos, Agustín
Publikováno v:
IMDEA Networks Institute Digital Repository
IMDEA Networks Institute
instname
IMDEA Networks Institute
instname
Recommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a0b9d150bbd46b11b36947c0d9839da1
https://hdl.handle.net/20.500.12761/1400
https://hdl.handle.net/20.500.12761/1400