对于另一个期望E_{s^\prime,r \sim p(s^\prime,r|s,a)}则不需要考虑重要性采样,因为其中所涉及到的p(s^\prime, r| s,a)是环境内部的状态转移概率,并没有涉及到任何一个策略,与重要性采样无关。 当然,有一些off-policy的算法也没有使用重要性采样,也不需要使用重要性采样, 比如经典的Q-Learning、D...
加入exploration又会降低学习效率,因此,在Q-learning中我们用了epsilon-greedy这种方法实现折衷。 off-policy与on-policy相比,off-policy用behavior policy得到的data来学习或者改进target policy,使得我们最后达到最优。具体来说,我们基于behavior policy(这个policy并不是最优的)来产生大量data,让agent进行探索。我们要从b...
Preference-based reinforcement learning (PbRL) develops agents using human preferences. Due to its empirical success, it has prospect of benefiting human-centered applications. Meanwhile, previous work on PbRL overlooks interpretability, which is an indispensable element of ethical artificial intelligence ...
Reinforcement learning (RL) has been applied to a wide range of motion control problems in robotics. In particular, policy gradient method (PGM) emerges as a powerful subset of RL that can learn effectively from one's experience. However, when the dynamics is stochastic and is short of sample...
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Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach. We focus on two types of off-policy estimators: model-based [Sutton and Barto, 1998; Mannor et al., 2007], and importance sampling weighting of the... J Pineau,A Guez,R Vincent,... - 《International Journal...
aFor those who lack learning motivation of students, teachers and parents should work together to help them realize the importance of learning. 为缺乏的那些人学会学生的刺激的,老师和父母应该帮助他们意识到学会的重要性。[translate]
Many studies have been conducted on the application of reinforcement learning (RL) to robots. A robot which is made for general purpose has redundant sensors or actuators because it is difficult to assume an environment that the robot will face and a task that the robot must execute. In this...
1 Off-policy learning methods Off-policy methods have an important role to play in the larger ambitions ... C Szepesvari - 《Advances in Neural Information Processing Systems》 被引量: 326发表: 2009年 Eligibility Traces for Off-Policy Policy Evaluation 2003. Optimality of reinforcement learning ...
R. S. Sutton and A. G. Barto. 2018.Reinforcement Learning: An Introduction .MIT Press....