… We tackle a basic and important problem in reinforcement learning: whether planning in an
empirical model is sample efficient to give an approximately optimal policy in the real model. …

… deep reinforcement learning to … reinforcement learning models, the framework aims to
devise optimal interest rate strategies that align with the banks’ objectives. Our empirical analyses …

… Automatic analysis tools are required to systematically search for unknown strategies and
their respective countermeasures. We propose deep reinforcement learning to learn attack …

… 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 …

… Learning a near optimal policy in a partially observable system remains an elusive … in
contemporary reinforcement learning. In this work, we consider episodic reinforcement learning in …

… These assumptions are removed for our more formal theoretical and empirical analysis and
should not be understood as limitations of RIS methods. We make the following assumptions…

… reinforcement learning systems. Much of the early work on representations for reinforcement
learning … In contrast, the idea behind deep reinforcement learning methods is that the agent …

… with the experimental (empirical) data. Second, … learning in reinforcement learning contexts,
we checked the idea that high exploration early in learning was related to better learning …

… Our empirical analysis through simulation yielded the following findings. First, compared
with the non-DRL approaches, the proposed DRL framework can, on average, generate 26.75…

… Reinforcement learning (RL) is a concept that has been invaluable to fields including machine
learning, neuroscience, and cognitive science. However, what RL entails differs between …