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Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space

机译：策略预测网络：在连续动作空间中的基于模型学习的无模型行为政策

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Zac Wellmer; James T. Kwok;
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1. Efficient reinforcement learning in continuous state and action spaces with Dyna and policy approximation [J] . Zhong Shan, Liu Quan, Zhang Zongzhang, Frontiers of computer science in China . 2019,第1期

机译：使用Dyna和策略逼近在连续状态和动作空间中进行有效的强化学习
2. Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces [J] . Daniel Hein, Alexander Hentschel, Thomas A. Runkler, International journal of swarm intelligence research . 2016,第3期

机译：在连续状态和动作空间中使用粒子群优化策略（PSO-P）进行强化学习
3. Policy Derivation Methods for Critic-Only Reinforcement Learning in Continuous Action Spaces [J] . Eduard Alibekov, Jiri Kubalik, Robert Babuska IFAC PapersOnLine . 2016,第5期

机译：连续动作空间中仅用于批判性强化学习的策略推导方法
4. Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space [C] . Zac Wellmer, James T. Kwok European conference on machine learning and principles and practice of knowledge discovery in databases . 2019

机译：策略预测网络：连续行动空间中基于模型的学习的无模型行为策略
5. Learning control policies from demonstration in continuous sensory and action space. [D] . McLeod, Adam M. 2015

机译：通过在连续的感官和动作空间中的演示来学习控制策略。
6. Correction: Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail [O] . Eleni Vasilaki, Nicolas Frémaux, Robert Urbanczik, 2009

机译：更正：在连续状态和动作空间中基于峰值的强化学习：当策略梯度方法失败时
7. Task complexity interacts with state-space uncertainty in the arbitration process between model-based and model-free reinforcement-learning at both behavioral and neural levels [O] . Dongjae Kim, Geon Yeong Park, John P. O’Doherty, 2018

机译：任务复杂性与在行为和神经水平的模型和无模型加强学习之间的仲裁过程中的状态空间不确定性相互作用

Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space

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