Archive of posts with category 'reinforcement-learning'
Perhaps we could use uncertainty estimation to detect where the model may be wrong and then correct for these potential errors without having to collect much more data. This uncertainty...
One of the inherent problems an agent faces in some arbitrary environment is how to decide whether to explore and discover more of the world around it, or to rather...
The active inference framework proposes agents act to maximise the evidence for a biased generative model, whereas in reinforcement learning the agent seeks to maximise the expected discounted cumulative reward....
But what is reinforcement learning? The field of reinforcement learning is at the crossroads between optimal control, animal psychology, artificial intelligence and game theory and has seen a surge of...
Where we are Up to this point we have discussed methods primarily relying on the learning of value functions, usually approximating these with some neural network. That is, our focus...
The World Models (Ha et al., 2018) paper presented at NIPS in 2018 exploits the idea of having an agent train entirely within its latent representation of the world it...
Model-free reinforcement learning algorithms are a class of algorithms which do not use the transition probability information to train and make decisions. In a sense, they are a class of...
