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Discrete-action reinforcement learning algorithms often falter in tasks with high-dimensional discrete action spaces due to the vast number of possible actions.
A markovian decision process
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Oron Anschel, Nir Baram, and Nahum Shimkin · 2017
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Socially aware motion planning with deep reinforcement learning
Yu Fan Chen, Michael Everett, Miao Liu, and Jonathan P How · 2017
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Q-learning in enormous action spaces via amortized approximate maximization
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Learning implicit credit assignment for cooperative multi-agent reinforcement learning
Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, and Yuk Ying Chung · 2020
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Randomized ensembled double q-learning: Learning fast without a model
Xinyue Chen, Che Wang, Zijian Zhou, and Keith Ross · 2021
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Starcraft ii: A new challenge for reinforcement learning
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning, 2018
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Deep reinforcement learning for autonomous driving: A survey
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Factored action spaces in deep reinforcement learning
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Distributional reinforcement learning
Marc G Bellemare, Will Dabney, and Mark Rowland · 2023
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Ensemble value functions for efficient exploration in multi-agent reinforcement learning
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Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
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