… learning algorithm needs to learn quickly from potential mistakes without needing to repeat
them multiple times before fixing them. Thus, learning on a real system requires an algorithm …

… Although off-policy learning algorithms may be part of the long-term answer, we do not
yet have a definitive solution. To quote Sutton and Barto [2018]: The potential for off-policy …

… However, some of the best model-free reinforcement learning algorithms require tens or
hundreds of millions of time steps – the equivalent of several weeks of training in real time. How …

… reinforcement learning agents. We show that naively adapting ETD(λ) to popular deep
reinforcement learning algorithms, … emphatic algorithms for use in the context of such algorithms, …

… However, the optimal performance of the scheduling system algorithm has high … (DQN)
algorithm in a deep reinforcement learning algorithm, which combines the Q-learning algorithm, …

… robot path planning based on the improved DDPG reinforcement learning algorithm. The
system uses the improved DDPG reinforcement learning algorithm. The content of this paper is …

… We present the first continuous control deep reinforcement learning algorithm which can
learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its …

… costs, we apply deep reinforcement learning (DRL) to IIoT … Therefore, we propose a FL
algorithm assisted by DRL, which … corroborates the effectiveness of the proposed algorithm. …

… Afterwards, Section 3 introduces and rigorously formalises the particular algorithmic trading
problem considered. Additionally, this section makes the link with the reinforcement learning …

… In this paper, a brief overview of the application of reinforcement algorithms in robotic
science is presented. This survey offered a comprehensive review based on segments as (1) …