摘要： Reinforcement learning is a type of machine learning in whicha computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment. This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task.
The goal of reinforcement learning in this case is to train the dog (agent) to complete a task within an environment, which includes the surroundings of the dog as well as the trainer. First, the trainer issues a command or cue, which the dog observes (observation). The dog then responds by taking an action. If the action is close to the desired behavior, the trainer will likely provide a reward, such asa food treat or a toy; otherwise, no reward or a negative reward will be provided.
At the beginning of training, the dog will likely take more random actions like rolling over when the command given is “sit,” as it is trying to associate specific observations with actions and rewards. This association, or mapping, between observations and actions is called policy.
From the dog’s perspective, the ideal case would be one in which it would respond correctly to every cue, so that it gets as many treats as possible. So, the whole meaning of reinforcement learning training is to “tune” the dog’s policy so that it learns the desired behaviors that will maximize some reward. After training is complete, the dogshould be able to observe the owner and take the appropriate action, for example, sitting when commanded to “sit” by using the internal policy it has developed. By this point, treats are welcome but shouldn’t be necessary (theoretically speaking!).
.....
Full Text： kdnuggets
若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： Big Data In Finance
留下你的回應
以訪客張貼回應