… In this section, we empirically analyze the performance of the κ-PI-DQN and κ-VI-DQN
algorithms on the Atari domains: Breakout, SpaceInvaders, Seaquest, Enduro, BeamRider, and …

… finite-time analysis of primal SRL algorithms with global optimality guarantee. Our empirical
… In reinforcement learning, we aim to find an optimal policy that maximizes the expected total …

… By collecting clear, informative and scalable experiments; and providing accessible tools
for reproducible evaluation we hope to facilitate progress in reinforcement learning research. …

… We empirically analyze the differences between the PR-MDP and NR-MDP approaches,
and demonstrate their ability to produce robust policies under abrupt perturbations and mass …

… to reinforcement learning models, procedures, techniques, and reinforcement learning … to
the agent-environment interface and how Reinforcement Learning can be used in various daily …

… Networked multi-agent reinforcement learning The agents in our networked control system
use reinforcement learning to learn how to map from states to actions. To study the effects of …

… In this work, we introduce the first such analysis. Namely, we formulate vari… Reinforcement
Learning algorithms fit well into our unified framework. We thus shed light on their empirical …

… They do this by finding solutions to a fundamental problem for Reinforcement Learning (RL),
the exploration-exploitation dilemma: when to take actions to obtain more information (…

… 4.2 Hierarchical Reinforcement Learning We consider posterior sampling for hierarchical
reinforcement learning (PSHRL) to be PSRL applied with a particular kind of prior distribution, …

… This article proposes a reinforcement learning (RL) framework embedded with data
envelopment analysis (DEA) to generate the optimal policy and guide the productivity improvement. …