Distributional reinforcement learning goes beyond the common approach to reinforcement learning and expected values, in that it focuses on the total reward or value of a return obtained as a consequence of an agent's choices—specifically, how this return behaves in a probabilistic perspective.
Of course, valuation of risky alternatives must reflect uncertainty ... and uncertainty about uncertainty. So we are not only interested in variation, but in jumps or rough volatility of variation ... in other words, consideration of alternatives amounts to stochastic calculus with the best approximations of rough volatility or partially rough volatility for the valuations of various options presented.
Distributional reinforcement learning goes beyond the common approach to reinforcement learning and expected values, in that it focuses on the total reward or value of a return obtained as a consequence of an agent's choices—specifically, how this return behaves in a probabilistic perspective.
Table of Contents Preface 1 Introduction 2 The Distribution of Returns 3 Learning the Return Distribution 4 Operators and Metrics 5 Distributional Dynamic Programming 6 Incremental Algorithms 7 Control 8 Statistical Functionals 9 Linear Function Approximation 10 Deep Reinforcement Learning 11 Two Applications and a Conclusion Notation Bibliography
Of course, valuation of risky alternatives must reflect uncertainty ... and uncertainty about uncertainty. So we are not only interested in variation, but in jumps or rough volatility of variation ... in other words, consideration of alternatives amounts to stochastic calculus with the best approximations of rough volatility or partially rough volatility for the valuations of various options presented.