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TA-Explore :
TA-Explore : Teacher-Assisted Exploration for Facilitating Fast Reinforcement Learning: Extended Abstract
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- Beikmohammadi, Ali, 1995- (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Magnússon, Sindri, 1987- (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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(creator_code:org_t)
- The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023
- 2023
- Engelska.
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Ingår i: AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. - : The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). - 9781450394321 ; , s. 2412-2414
- Relaterad länk:
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Abstract
Ämnesord
Stäng
- Reinforcement Learning (RL) is crucial for data-driven decision-making but suffers from sample inefficiency. This poses a risk to system safety and can be costly in real-world environments with physical interactions. This paper proposes a human-inspired framework to improve the sample efficiency of RL algorithms, which gradually provides the learning agent with simpler but similar tasks that progress toward the main task. The proposed method does not require pre-training and can be applied to any goal, environment, and RL algorithm, including value-based and policy-based methods, as well as tabular and deep-RL methods. The framework is evaluated on a Random Walk and optimal control problem with constraint, showing good performance in improving the sample efficiency of RL-learning algorithms.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- PPO
- policy optimization
- exploration
- deep RL
- sample efficiency
- data- och systemvetenskap
- Computer and Systems Sciences
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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