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Markov Decision Processes in Artificial Intelligence by Olivier Buffet, Olivier Sigaud

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Index

Symbols

AO*

LAO*

Rmax

SARSA(λ)

CoMDP

ADD, see algebraic decision diagrams

DEC-MDP

NOMDP

POMDP

transient

DEC-POMDP

BDD, see binary decision diagrams

A

abalone

action

continuous

actor-critic

natural (NAC)

adaptive dynamics learning (ADL)

adaptive play

admissible heuristic

agent

playing

algebraic backward induction

algorithm

envelope

offline

online

Shapley’s algorithm

simulation-based

approximation

capacity

operator

power

theory

B

Bayesian networks

dynamic

Bellman

operator

optimality principle

Bellman equation

algebraic

for POMDPs

possibilistic

bias optimality

bias-variance trade-off

biodiversity

C

co-evolution

communication

controlled stochastic process

covering number

criterion

γ-discounted

adapted average

average

finite

total reward

D

decision diagrams

algebraic

binary

decision trees

depth-width trade-off

deviation matrix

discount factor

dyna

dyna-Q

focused

dynamic programming

approximate

asynchronous

exact methods

for DEC-POMDPs

possibilistic

E

E3

ED-Dec-MDP

efficient play

eligibility trace

empirical error

equilibrium

in dominating strategies

in mixed strategies

Nash equilibrium

Nash equilibrium in mixed strategies

error

generalization

learning

temporal difference

expected payoff

expected utility

generalized

qualitative binary possibilistic

qualitative optimistic

qualitative pessimistic

experience replay

exploration/exploitation trade-off

F

factored policy gradient (FPG)

feature

vector

fictitious play

finite difference method

focused reinforcement learning

function approximation ...

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