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Followup to: How to Be Oversurprised A Bayesian update needs never lose information. In a dynamic world, though, the update is only half t

Followup to: How to Disentangle the Past and the Future Some agents are memoryless, reacting to each new observation as it happens, withou

I'm on my way to an important meeting. Am I worried? I'm not worried. The presentation is on my laptop. I distinctly remember putting it the

Followup to: The Bayesian Agent This post explains one interesting and influential algorithm for achieving high utility of the actions of

Edit: Following mixed reception, I decided to split this part out of the latest post in my sequence on reinforcement learning. It wasn't cle

Followup to: Reinforcement Learning: A Non-Standard Introduction, Reinforcement, Preference and Utility A reinforcement-learning agent int

Followup to: Reinforcement Learning: A Non-Standard Introduction A reinforcement-learning agent is interacting with its environment throug

Followup to: Part 1 In part 1 we modeled the dynamics of an agent and its environment as a turn-based discrete-time process. We now start

Imagine that the world is divided into two parts: one we shall call the agent and the rest - its environment. Imagine you could describe in

Would readers be interested in a sequence of posts offering an intuitive explanation of my underway thesis on the application of information