The Experts below are selected from a list of 10725 Experts worldwide ranked by ideXlab platform
Ronnie Belmans - One of the best experts on this subject based on the ideXlab platform.
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Learning Agent for a heat pump thermostat with a set back strategy using model free reinforcement Learning
Energies, 2015Co-Authors: Frederik Ruelens, Sandro Iacovella, Bert Claessens, Ronnie BelmansAbstract:The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a Learning Agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g., when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. The first challenge is that for most residential buildings, a description of the thermal characteristics of the building is unavailable and challenging to obtain. The second challenge is that the relevant information on the state, i.e., the building envelope, cannot be measured by the Learning Agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement Learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed Learning Agent can reduce the energy consumption by 4%–9% during 100 winter days and by 9%–11% during 80 summer days compared to the conventional constant set-point strategy.
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Learning Agent for a heat pump thermostat with a set back strategy using model free reinforcement Learning
arXiv: Systems and Control, 2015Co-Authors: Frederik Ruelens, Sandro Iacovella, Bert Claessens, Ronnie BelmansAbstract:The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a Learning Agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the Learning Agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement Learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed Learning Agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategy
Marcus Hutter - One of the best experts on this subject based on the ideXlab platform.
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On the computability of Solomonoff induction and AIXI
Theoretical Computer Science, 2018Co-Authors: Jan Leike, Marcus HutterAbstract:Abstract How could we solve the machine Learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement Learning Agent AIXI are proposed answers to this question. Both are known to be incomputable. We quantify this using the arithmetical hierarchy, and prove upper and in most cases corresponding lower bounds for incomputability. Moreover, we show that AIXI is not limit computable, thus it cannot be approximated using finite computation. However there are limit computable e -optimal approximations to AIXI. We also derive computability bounds for knowledge-seeking Agents, and give a limit computable weakly asymptotically optimal reinforcement Learning Agent.
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ALT - On the Computability of Solomonoff Induction and Knowledge-Seeking
Lecture Notes in Computer Science, 2015Co-Authors: Jan Leike, Marcus HutterAbstract:Solomonoff induction is held as a gold standard for Learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking Agents, and give a limit-computable weakly asymptotically optimal reinforcement Learning Agent.
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On the Computability of Solomonoff Induction and Knowledge-Seeking
arXiv: Artificial Intelligence, 2015Co-Authors: Jan Leike, Marcus HutterAbstract:Solomonoff induction is held as a gold standard for Learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking Agents, and give a limit-computable weakly asymptotically optimal reinforcement Learning Agent.
Frederik Ruelens - One of the best experts on this subject based on the ideXlab platform.
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Learning Agent for a heat pump thermostat with a set back strategy using model free reinforcement Learning
Energies, 2015Co-Authors: Frederik Ruelens, Sandro Iacovella, Bert Claessens, Ronnie BelmansAbstract:The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a Learning Agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g., when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. The first challenge is that for most residential buildings, a description of the thermal characteristics of the building is unavailable and challenging to obtain. The second challenge is that the relevant information on the state, i.e., the building envelope, cannot be measured by the Learning Agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement Learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed Learning Agent can reduce the energy consumption by 4%–9% during 100 winter days and by 9%–11% during 80 summer days compared to the conventional constant set-point strategy.
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Learning Agent for a heat pump thermostat with a set back strategy using model free reinforcement Learning
arXiv: Systems and Control, 2015Co-Authors: Frederik Ruelens, Sandro Iacovella, Bert Claessens, Ronnie BelmansAbstract:The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a Learning Agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the Learning Agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement Learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed Learning Agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategy
Jan Leike - One of the best experts on this subject based on the ideXlab platform.
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On the computability of Solomonoff induction and AIXI
Theoretical Computer Science, 2018Co-Authors: Jan Leike, Marcus HutterAbstract:Abstract How could we solve the machine Learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement Learning Agent AIXI are proposed answers to this question. Both are known to be incomputable. We quantify this using the arithmetical hierarchy, and prove upper and in most cases corresponding lower bounds for incomputability. Moreover, we show that AIXI is not limit computable, thus it cannot be approximated using finite computation. However there are limit computable e -optimal approximations to AIXI. We also derive computability bounds for knowledge-seeking Agents, and give a limit computable weakly asymptotically optimal reinforcement Learning Agent.
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ALT - On the Computability of Solomonoff Induction and Knowledge-Seeking
Lecture Notes in Computer Science, 2015Co-Authors: Jan Leike, Marcus HutterAbstract:Solomonoff induction is held as a gold standard for Learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking Agents, and give a limit-computable weakly asymptotically optimal reinforcement Learning Agent.
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On the Computability of Solomonoff Induction and Knowledge-Seeking
arXiv: Artificial Intelligence, 2015Co-Authors: Jan Leike, Marcus HutterAbstract:Solomonoff induction is held as a gold standard for Learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking Agents, and give a limit-computable weakly asymptotically optimal reinforcement Learning Agent.
Christian R Shelton - One of the best experts on this subject based on the ideXlab platform.
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Agents - A social reinforcement Learning Agent
Proceedings of the fifth international conference on Autonomous agents - AGENTS '01, 2001Co-Authors: Charles L Isbell, Satinder Singh, Christian R Shelton, Michael Kearns, Peter StoneAbstract:We report on our reinforcement Learning work on Cobot, a software Agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot~\cite{cobotaaai} provided him with the ability to collect {\em social statistics\/} and report them to users in a reactive manner. Here we describe our application of reinforcement Learning to allow Cobot to proactively take actions in this complex social environment, and adapt his behavior from multiple sources of human reward. After 5 months of training, Cobot received 3171 reward and punishment events from 254 different Lambda\-MOO users, and learned nontrivial preferences for a number of users. Cobot modifies his behavior based on his current state in an attempt to maximize reward. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the Learning experiment.
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cobot a social reinforcement Learning Agent
Neural Information Processing Systems, 2001Co-Authors: Charles L Isbell, Christian R SheltonAbstract:We report on the use of reinforcement Learning with Cobot, a software Agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the ability to collect social statistics and report them to users. Here we describe an application of RL allowing Cobot to take proactive actions in this complex social environment, and adapt behavior from multiple sources of human reward. After 5 months of training, and 3171 reward and punishment events from 254 different LambdaMOO users, Cobot learned nontrivial preferences for a number of users, modifing his behavior based on his current state. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the Learning experiment.
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NIPS - Cobot: A Social Reinforcement Learning Agent
2001Co-Authors: Charles L Isbell, Christian R SheltonAbstract:We report on the use of reinforcement Learning with Cobot, a software Agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the ability to collect social statistics and report them to users. Here we describe an application of RL allowing Cobot to take proactive actions in this complex social environment, and adapt behavior from multiple sources of human reward. After 5 months of training, and 3171 reward and punishment events from 254 different LambdaMOO users, Cobot learned nontrivial preferences for a number of users, modifing his behavior based on his current state. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the Learning experiment.