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Multi-objectivization of reinforcement learning problems by reward shaping
2014
2014 International Joint Conference on Neural Networks (IJCNN)
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In this paper we investigate the multi-objectivization of reinforcement learning problems. ...
Reward shaping is a technique to speed up reinforcement learning by including additional heuristic knowledge in the reward signal. ...
Then we formulate the multi-objectivization of a reinforcement learning problem by reward shaping in Section IV, and discuss some theoretical properties thereof. ...
doi:10.1109/ijcnn.2014.6889732
dblp:conf/ijcnn/BrysHVTKN14
fatcat:5xgyf7xdsjdfdephtcsp2axipa
Special issue on multi-objective reinforcement learning
2017
Neurocomputing
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We also wish to thank the editors of Neurocomputing who supervised an independent review process for those papers for which we had a conflict of interest. ...
Acknowledgements We would like to thank all of the authors who submitted their work for this issue, as well as the reviewers who generously gave their time and expertise during the review process. ...
On top of the multi-objectivization mechanism, reward shaping is used to incorporate heuristical knowledge. The goal is to learn the Pareto front of optimal policies. ...
doi:10.1016/j.neucom.2017.06.020
fatcat:bw6mnryx3fbitm7rnj6wehopsm
Reinforcement Learning on Multiple Correlated Signals
2014
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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This extended abstract provides a brief overview of my PhD research on multi-objectivization and ensemble techniques in reinforcement learning. ...
a linear scalarization (implicit multi-objectivization). ...
One example of a set of techniques that holds much promise is ensemble techniques for reinforcement learning (Wiering and van Hasselt 2008) . ...
doi:10.1609/aaai.v28i1.8773
fatcat:z23vcwamnzfz7akupg5iobyday
Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence
2014
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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This class of problems is very relevant in reinforcement learning, as any single-objective reinforcement learning problem can be framed as such a multi-objective problem using multiple reward shaping functions ...
Multi-objective problems with correlated objectives are a class of problems that deserve specific attention. ...
Acknowledgments Tim Brys is funded by a Ph.D grant of the Research Foundation-Flanders (FWO). This work was supported in part by NSF IIS-1149917 and NSF IIS-1319412. ...
doi:10.1609/aaai.v28i1.8998
fatcat:s3wv6tvppngrzpbcubpkdtik7e
Risk-sensitivity through multi-objective reinforcement learning
2015
2015 IEEE Congress on Evolutionary Computation (CEC)
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Usually in reinforcement learning, the goal of the agent is to maximize the expected return. ...
Our approach is based on multi-objectivization where a standard single-objective environment is extended with one (or more) additional objectives. ...
Multi-objectivization was introduced in reinforcement learning to incorporate multiple pieces of heuristic knowledge in order to speed up learning [4] , [5] . ...
doi:10.1109/cec.2015.7257098
dblp:conf/cec/MoffaertBN15
fatcat:oc4wb34t6bfenctn7dz4sdpwwm
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2001.06921v1 fatcat:f2hur3cod5hwbanr2w44by3uge
Reinforcement learning is one of the core components in designing an artificial intelligent system emphasizing real-time response. ...
First, we illustrated the core techniques of reinforcement learning in an easily understandable and comparable way. ...
[4] proposed a novel method for the multi-objectivization of Markov Decision Problem through the use of multiple reward shaping functions. ...
arXiv:2001.06921v2
fatcat:uwqn4jmginf73ouk3zmm45uozy
Reinforcement learning agents providing advice in complex video games
2014
Connection science
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Reinforcement learning transfer using a sparse coded inter-task mapping. In LNAI Post-proceedings of the European Workshop on Multi-agent Systems. ...
Transfer learning via multiple inter-task mappings. ...
Multi-objectivization of reinforcement learning problems by reward shaping. ...
doi:10.1080/09540091.2014.885279
fatcat:ept2qvn4n5aktjv37ty6vk6vwy
Off-Policy Shaping Ensembles in Reinforcement Learning
[article]
2014
arXiv
pre-print
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This opens up new possibilities for sound ensemble techniques in reinforcement learning. In this work we propose learning an ensemble of policies related through potential-based shaping rewards. ...
Learning happens in real time, and we empirically show the combination policy to outperform the individual policies of the ensemble. ...
a multi-objectivization formalism demonstrate its usefullness while treating different shapings as correlated objectives [4] . ...
arXiv:1405.5358v1
fatcat:ce7lvoz2knemtaairmh5ndfenu
Hierarchical Potential-based Reward Shaping from Task Specifications
[article]
2022
arXiv
pre-print
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The automatic synthesis of autonomous agents' policies through reinforcement learning relies on the definition of a reward signal that simultaneously captures many, possibly conflicting, requirements of ...
In this work, we introduce a novel, hierarchical, potential-based reward-shaping approach (HPRS) for defining effective rewards for a large family of problems. ...
multi-objectivization of the task and solves the multi-objective problem by linear scalarization. ...
arXiv:2110.02792v2
fatcat:e7wj377xvffcpegfmuqr3izudi
Transfer learning for direct policy search: A reward shaping approach
2013
2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)
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In the perspective of life long learning, a robot may face different, but related situations. ...
Afterwards, the knowledge base is exploited on a target task, with a reward shaping approach: besides its reward on the task, a policy is credited with a reward computed from the knowledge base. ...
ACKNOWLEDGMENT This work is supported by the ANR CreAdapt project (ANR-12-JS03-0009). ...
doi:10.1109/devlrn.2013.6652568
dblp:conf/icdl-epirob/Doncieux13
fatcat:djiyxqhmmfgwrkmrnlmvrccomi
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons
[article]
2020
arXiv
pre-print
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Traditionally, this parameter was considered part of the MDP; however, as deep reinforcement learning algorithms tend to become unstable when the effective planning horizon is long, recent works refer ...
In reinforcement learning, the discount factor γ controls the agent's effective planning horizon. ...
Broader Impact Reward tweaking tackles a fundamental problem in applied reinforcement learning. ...
arXiv:2002.03327v2
fatcat:kucukgik7jdoznquxeb7q6gegy
Optimal Control Based on CACM-RL in a Two-Wheeled Inverted Pendulum
2012
International Journal of Advanced Robotic Systems
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CACM-RL in this kind of system. ...
Learning while maintaining the equilibrium is a complex task. It is easy in stable platforms because the system never reaches an unstable state, but in unstable systems it is very difficult. ...
Reinforcement Learning Reinforcement learning methods only require a scalar reward (or punishment) to learn to map situations (states) in actions [10] . ...
doi:10.5772/54658
fatcat:asws5d22gvgjjd2v4qrih5cfpy
Object Affordance Driven Inverse Reinforcement Learning Through Conceptual Abstraction and Advice
2018
Paladyn: Journal of Behavioral Robotics
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Within human Intent Recognition (IR), a popular approach to learning from demonstration is Inverse Reinforcement Learning (IRL). ...
An architecture for recognizing human intent is presented which consists of an extended Maximum Likelihood Inverse Reinforcement Learning agent. ...
Acknowledgement: The authors would also like to thank Zubin Bhuyan, University of Massachusetts Lowell, for the discussion regarding IRL. ...
doi:10.1515/pjbr-2018-0021
fatcat:m6a2fm5ja5elnp25prgy6mnjp4
Traditional Narratives of Higher Education Cultural Tensions and Ethical Considerations in Adult Learning
2020
Journal of Critical Thought and Praxis
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A contemplative model of higher education is shared, supporting meaningful forms of adult learning via compassion/self-compassion, active listening, and mindfulness practices. ...
With the support of holistic human development and learning theories, I advocate for more integrative approaches to higher education. ...
For many adult learners, the higher education experience may feel like a high-stakes learning environment to prove self-worth by way of external rewards, eliciting undesirable feelings like that of anxiety ...
doi:10.31274/jctp.9744
fatcat:mrkvph7gdzbpfpsqz6dcgkttp4
Real Time Demand Response Modelling for Residential Consumers in Smart Grid Considering Renewable Energy with Deep Learning Approach
2021
IEEE Access
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By adopting deep RL (DRL) approach aforesaid problems can be rectified by enabling end-to-end learning capability of deep neural networks [22] . ...
Due to recent advancement in artificial intelligence, reinforcement learning(RL) gained more attention to provide a solution to the decision-making problem in smart grid [45] . ...
In 2019 and 2020 he received the award as Highly cited Researcher by ISI Web of Science Group Engineering, Isfahan University of Technology. ...
doi:10.1109/access.2021.3071993
fatcat:5opfbqejqzdqneiffklcetwsam
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