Reinforcement learning, according to our recent publication, deals with a unique problem setting where a random agent tries to learn the best way to interact with the environment. In exchange for his actions, he receives delayed shortcuts, also known as rewards; the agent's ultimate goal is to find the optimal policy that maximizes the cumulative numerical return https://perfectial.com/blog/q-learning-applications/
RL-based technologies have already been implemented by inventive companies to optimally configure multi-level web networks, build reliable recommendation algorithms, develop complex intrusion detection schemes for IoT networks, and the like.
Igor Grovich
Reinforcement learning, according to our recent publication, deals with a unique problem setting where a random agent tries to learn the best way to interact with the environment. In exchange for his actions, he receives delayed shortcuts, also known as rewards; the agent's ultimate goal is to find the optimal policy that maximizes the cumulative numerical return https://perfectial.com/blog/q-learning-applications/
RL-based technologies have already been implemented by inventive companies to optimally configure multi-level web networks, build reliable recommendation algorithms, develop complex intrusion detection schemes for IoT networks, and the like.