2,351 research outputs found
Improved Reinforcement Learning with Curriculum
Humans tend to learn complex abstract concepts faster if examples are
presented in a structured manner. For instance, when learning how to play a
board game, usually one of the first concepts learned is how the game ends,
i.e. the actions that lead to a terminal state (win, lose or draw). The
advantage of learning end-games first is that once the actions which lead to a
terminal state are understood, it becomes possible to incrementally learn the
consequences of actions that are further away from a terminal state - we call
this an end-game-first curriculum. Currently the state-of-the-art machine
learning player for general board games, AlphaZero by Google DeepMind, does not
employ a structured training curriculum; instead learning from the entire game
at all times. By employing an end-game-first training curriculum to train an
AlphaZero inspired player, we empirically show that the rate of learning of an
artificial player can be improved during the early stages of training when
compared to a player not using a training curriculum.Comment: Draft prior to submission to IEEE Trans on Games. Changed paper
slightl
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
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