… In reinforcement learning, state representations are used to tractably … empirical survey of
classic representation learning methods from the literature and results on the Arcade Learning …

… Recently, there are many efforts attempting to learn useful policies for continuous control
in visual reinforcement learning (RL). In this scenario, it is important to learn a generalizable …

… reinforcement learning literature. In this paper we study value-based reinforcement learning
… We show that a value-based reinforcement learning agent cannot converge to a policy that …

… In Moody, Wu, Liao & Saffell (1998), we demonstrate that reinforcement learning provides
a more elegant and effective means for training trading systems when transaction costs are …

… Overall, this work identifies new challenges for empirical rigor in reinforcement learning, …
An empirical analysis of value function-based and policy search reinforcement learning. In …

… , we view reinforcement learning as a two-player game: The reinliorcenient-learning algorithm
selects … Reinforcement learning in deterministic state spaces is simply a special case of …

… development, reinforcement learning theory, and … of reinforcement learning with deep
learning techniques has propelled autonomous systems to new heights, enabling them to learn …

… In this paper, we consider the episodic reinforcement learning setting in which the agent
accesses p and r by interacting with the environment over successive episodes, ie, the agent …

… In reinforcement learning, an agent incrementally refines a … be characterized as explicit
reinforcement learning, as it deals … a symbolic variant of reinforcement learning, in which sets of …

In reinforcement learning (RL) research, it is common to assume access to direct online
interactions with the environment. However in many real-world applications, access to the …