(Future rewards beyond the horizon may be considered by the planning algorithm through the use of a learned value function.) The agent then executes the first action of the plan, and immediately discards the res
The first one is to maximize the expected return, the same as in traditional RL algorithms. The other one is to encourage the student agent to follow the guidance provided by the teacher. As the student agent’s expertise increases during the training process, the weight assigned to the ...
Gaussian Process-based Model Predictive Control (GP-MPC) integrates Gaussian Process (GP) regression with traditional MPC to enhance the controller's ability to handle model uncertainties and non-linear dynamics. This hybrid approach leverages the strengths of both MPC and GP to provide a more robu...
Better Response: I will need a bit of information to provide you with a recipe. I can provide you with some typical ingredients to the dish, but it would be really useful if you can help me with some of the details. What is the type of dish? A breakfast dish? Is it traditional to...
With the expanding application of industrial robots, the complexity of robotic tasks has increased, rendering traditional human–machine interaction methods, such as joysticks and control panels, insufficient to meet the needs of today’s diverse production tasks [1]. The collaborative control strategy ...
(TLFRL) in IP-over-fixed/flex-grid optical networks. The main target of TLFRL is to reduce the need to reallocate the spectrum by lowering the fragmentation and blocking probability. We achieve this by leveraging advanced demand organization techniques while using traditional networking infrastructure...
the same as in traditional RL algorithms. The other one is to encourage the student agent to follow the guidance provided by the teacher. As the student agent’s expertise increases during the training process, the weight assigned to the second objective gradually decreases over time, reducing it...
In such a setup, a fair comparison is extremely important; hence, we must ensure that both methods can utilize the same number of training samples and tune their neural network with the same number of iterations. The RRT-RL combined method does not utilize the traditional training loop concept...
However, in a multi-agent system, the traditional reinforcement learning algorithm uses extrinsic reward to guide agents to adjust their own policy. The agents take actions in environment to interact with the environment. When the policy is correct, it will get a positive reward value, otherwise ...