Experimental quantum speed-up in reinforcement learning agents
نویسندگان
چکیده
منابع مشابه
Searching for Plannable Domains can Speed up Reinforcement Learning
Reinforcement learning (RL) involves sequential decision making in uncertain environments. The aim of the decision-making agent is to maximize the benefit of acting in its environment over an extended period of time. Finding an optimal policy in RL may be very slow. To speed up learning, one often used solution is the integration of planning, for example, Sutton’s Dyna algorithm, or various oth...
متن کاملEligibility Propagation to Speed up Time Hopping for Reinforcement Learning
A mechanism called Eligibility Propagation is proposed to speed up the Time Hopping technique used for faster Reinforcement Learning in simulations. Eligibility Propagation provides for Time Hopping similar abilities to what eligibility traces provide for conventional Reinforcement Learning. It propagates values from one state to all of its temporal predecessors using a state transitions graph....
متن کاملEncoding and Combining Knowledge to Speed up Reinforcement Learning
Reinforcement learning algorithms typically require too many ‘trial-and-error’ experiences before reaching a desirable behaviour. A considerable amount of ongoing research is focused on speeding up this learning process by using external knowledge. We contribute in several ways, proposing novel approaches to transfer learning and learning from demonstration, as well as an ensemble approach to c...
متن کاملUsing Case Based Heuristics to Speed up Reinforcement Learning
The aim of this work is to combine three successful AI techniques –Reinforcement Learning (RL), Heuristics Search and Case Based Reasoning (CBR)– creating a new algorithm that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging tech...
متن کاملHierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents
This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nature
سال: 2021
ISSN: 0028-0836,1476-4687
DOI: 10.1038/s41586-021-03242-7