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What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience
In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about environment observations are used as additional input features to enable better goal-directed decision-making.
Should Models Be Accurate?
We focus our investigations on Dyna-style planning in a prediction setting.
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations.
What's a Good Prediction? Challenges in evaluating an agent's knowledge
Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems.
The Forget-me-not Process
We introduce the Forget-me-not Process, an efficient, non-parametric meta-algorithm for online probabilistic sequence prediction for piecewise stationary, repeating sources.
