Reinforcement learning is a type of machine learning that is concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. The problem is formally described by the Markov decision process (MDP).RL algorithms are used in autonomous vehicles, robotics, fault detection, telecommunications, and many other…
Tag: reinforcement-learning
Robotics
The problem of robotics is one that has been tackled by a number of different AI paradigms over the years. However, the current state of the art is still far from being able to create robots that are able to operate in unstructured environments as effectively as humans can. This is largely due to the…
Active learning
The vast majority of machine learning models are trained using a static dataset; that is, the dataset is fixed and does not change over time. This can be a problem when dealing with time-sensitive data, as the model may not be able to adapt to changes in the underlying data distribution. Active learning is a…
Reinforcement learning
Reinforcement learning is a subfield of machine learning that deals with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. Reinforcement learning algorithms have been applied successfully to problems like checkers, backgammon, and other board games, to bicycle balancing, networked control systems, robot motion planning, and protein…
Hidden Markov models that are difficult to train
There are a number of different ways to go about using AI to learn the patterns in hidden Markov models. One approach is to use a technique called deep learning. Deep learning is a type of machine learning that is particularly well suited to learning patterns in data. Deep learning is a subset of machine…
Support vector machines that are difficult to train
There are a number of ways in which artificial intelligence (AI) can help to train support vector machines (SVMs) more effectively. These methods can improve the performance of the SVM and make it more robust to different types of data. One approach is to use reinforcement learning (RL) to directly optimize the parameters of the…
Reinforcement learning agents that get stuck in local minima
The problem of getting stuck in local minima is a common one for reinforcement learning agents. There are a few ways to overcome this problem. One way is to use a technique called last-mile optimization. With last-mile optimization, the agent tries to find the global optimum by starting from the local optimum and then moving…