RLnätverksbaserade
RLnätverksbaserade, often translated as RL network-based, refers to a category of algorithms and systems that leverage reinforcement learning (RL) principles to operate within or manage network infrastructure. These approaches typically involve agents that learn optimal policies for network tasks through trial and error, interacting with the network environment to maximize a defined reward signal. The "network-based" aspect signifies that the core application or problem domain lies within computer networks, encompassing areas such as routing, resource allocation, congestion control, and network security.
The fundamental idea behind RLnätverksbaserade systems is to enable networks to adapt and optimize their behavior
Challenges in implementing RLnätverksbaserade solutions often involve the complexity of network environments, the need for efficient