Distributional Reinforcement Learning

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  • The
  • first
  • comprehensive
  • guide
  • to
  • distributional
  • reinforcement
  • learning,
  • providing
  • a
  • new
  • mathematical
  • formalism
  • for
  • thinking
  • about
  • decisions
  • from
  • a
  • probabilistic
  • perspective.Distributional
  • reinforcement
  • learning
  • is
  • a
  • new
  • mathematical
  • formalism
  • for
  • thinking
  • about
  • decisions.
  • Going
  • beyond
  • the
  • common
  • approach
  • to
  • reinforcement
  • learning
  • and
  • expected
  • values,
  • it
  • focuses
  • on
  • the
  • total
  • reward
  • or
  • return
  • obtained
  • as
  • a
  • consequence
  • of
  • an
  • agent's
  • choices—specifically,
  • how
  • this
  • return
  • behaves
  • from
  • a
  • probabilistic
  • perspective.
  • In
  • this
  • first
  • comprehensive
  • guide
  • to
  • distributional
  • reinforcement
  • learning,
  • Marc
  • G.
  • Bellemare,
  • Will
  • Dabney,
  • and
  • Mark
  • Rowland,
  • who
  • spearheaded
  • development
  • of
  • the
  • field,
  • present
  • its
  • key
  • concepts
  • and
  • review
  • some
  • of
  • its
  • many
  • applications.
  • They
  • demonstrate
  • its
  • power
  • to
  • account
  • for
  • many
  • complex,
  • interesting
  • phenomena
  • that
  • arise
  • from
  • interactions
  • with
  • one's
  • environment.The
  • authors
  • present
  • core
  • ideas
  • from
  • classical
  • reinforcement
  • learning
  • to
  • contextualize
  • distributional
  • topics
  • and
  • include
  • mathematical
  • proofs
  • pertaining
  • to
  • major
  • results
  • discussed
  • in
  • the
  • text.
  • They
  • guide
  • the
  • reader
  • through
  • a
  • series
  • of
  • algorithmic
  • and
  • mathematical
  • developments
  • that,
  • in
  • turn,
  • characterize,
  • compute,
  • estimate,
  • and
  • make
  • decisions
  • on
  • the
  • basis
  • of
  • the
  • random
  • return.
  • Practitioners
  • in
  • disciplines
  • as
  • diverse
  • as
  • finance
  • (risk
  • management),
  • computational
  • neuroscience,
  • computational
  • psychiatry,
  • psychology,
  • macroeconomics,
  • and
  • robotics
  • are
  • already
  • using
  • distributional
  • reinforcement
  • learning,
  • paving
  • the
  • way
  • for
  • its
  • expanding
  • applications
  • in
  • mathematical
  • finance,
  • engineering,
  • and
  • the
  • life
  • sciences.
  • More
  • than
  • a
  • mathematical
  • approach,
  • distributional
  • reinforcement
  • learning
  • represents
  • a
  • new
  • perspective
  • on
  • how
  • intelligent
  • agents
  • make
  • predictions
  • and decisions.
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