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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 12: AAAI-21 Technical Tracks 12

Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework

February 1, 2023

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Authors

Chuheng Zhang

IIIS, Tsinghua University


Yuanying Cai

IIIS, Tsinghua University


Longbo Huang

IIIS, Tsinghua University


Jian Li

IIIS, Tsinghua University


DOI:

10.1609/aaai.v35i12.17297


Abstract:

Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we consider a reward-free RL framework that completely separates exploration from exploitation and brings new challenges for exploration algorithms. In the exploration phase, the agent learns an exploratory policy by interacting with a reward-free environment and collects a dataset of transitions by executing the policy. In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment. This framework is suitable for the meta RL setting where there are many reward functions of interest. In the exploration phase, we propose to maximize the Renyi entropy over the state-action space and justify this objective theoretically. The success of using Renyi entropy as the objective results from its encouragement to explore the hard-to-reach state-actions. We further deduce a policy gradient formulation for this objective and design a practical exploration algorithm that can deal with complex environments. In the planning phase, we solve for good policies given arbitrary reward functions using a batch RL algorithm. Empirically, we show that our exploration algorithm is effective and sample efficient, and results in superior policies for arbitrary reward functions in the planning phase.

Topics: AAAI

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HOW TO CITE:

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework Proceedings of the AAAI Conference on Artificial Intelligence (2021) 10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework AAAI 2021, 10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li (2021). Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework. Proceedings of the AAAI Conference on Artificial Intelligence, 10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li. Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li. 2021. Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework. "Proceedings of the AAAI Conference on Artificial Intelligence". 10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li. (2021) "Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework", Proceedings of the AAAI Conference on Artificial Intelligence, p.10859-10867

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li, "Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework", AAAI, p.10859-10867, 2021.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li. "Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li. "Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 10859-10867.

Chuheng Zhang||Yuanying Cai||Longbo Huang||Jian Li. Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework. AAAI[Internet]. 2021[cited 2023]; 10859-10867.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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101, Palo Alto, California 94303 All Rights Reserved

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