4 天前 - … of the proposed adaptive algorithm for specific error types, … adaptive algorithms
based on reinforcement deep learning … We use the most advanced Deep reinforcement learning …

4 天前 - … We have implemented this algorithm and present extensive experimental results
on challenging scenarios involving aircraft models, multi-agent systems, realistic simulators, …

4 天前 - … This paper introduces a novel hierarchical reinforcement learning-based
algorithm to address the critical limitation of the aerial robot’s battery life. Central to our …

4 天前 - … Reinforcement Learning algorithms for finding optimal routes in a packet-optical
network scenario. The algorithm … Then, the algorithm executes Q-learning based on this set of …

4 天前 - … -learning ideas into reinforcement learning algorithms and propose a new model
named MetaNet. Our results show that reinforcement learning algorithms … -learning methods, …

5 天前 - … on multi-agent reinforcement learning and double-layer … Finally, a multi-agent
algorithm DLMQMIX is proposed to … learning algorithms have better scalability and can adapt …

5 天前 - … To assist with this, we proposed a NAS algorithm using reinforcement learning.
The important point is that if the value is large based on a certain probability R L ( p r o b ), the …

5 天前 - … This paper presents a model that utilizes deep reinforcement learning (DRL) and
the Deep Q-learning (DQN) algorithm to facilitate ethical decision-making in self-driving cars. …

5 天前 - … a deep distributional reinforcement learning algorithm to tackle collision avoidance
and local path planning problems. This study establishes an algorithmic model and designs …

5 天前 - … -tuned these algorithms to the peculiarities of network traffic patterns and attack
behaviors, requiring a deep understanding of both cybersecurity and reinforcement learning. …