1,777 research outputs found
Learning Behaviors of Stochastic Automata and Some Applications
It is known that stochastic automata can be applied to describe the behavior of a decision maker or manager in the condition of uncertainty. This paper discusses learning behaviors of stochastic automata under unknown nonstationary multi-teacher environment. The consistency of sequential decision making procedures is proved under some mild conditions
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. This approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the design of practical algorithms. We answer this hitherto open
question in the affirmative, by providing the first computationally feasible
approximation to the AIXI agent. To develop our approximation, we introduce a
Monte Carlo Tree Search algorithm along with an agent-specific extension of the
Context Tree Weighting algorithm. Empirically, we present a set of encouraging
results on a number of stochastic, unknown, and partially observable domains.Comment: 8 LaTeX pages, 1 figur
Learning Task Specifications from Demonstrations
Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.Comment: NIPS 201
Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet
Various optimality properties of universal sequence predictors based on
Bayes-mixtures in general, and Solomonoff's prediction scheme in particular,
will be studied. The probability of observing at time , given past
observations can be computed with the chain rule if the true
generating distribution of the sequences is known. If
is unknown, but known to belong to a countable or continuous class \M
one can base ones prediction on the Bayes-mixture defined as a
-weighted sum or integral of distributions \nu\in\M. The cumulative
expected loss of the Bayes-optimal universal prediction scheme based on
is shown to be close to the loss of the Bayes-optimal, but infeasible
prediction scheme based on . We show that the bounds are tight and that no
other predictor can lead to significantly smaller bounds. Furthermore, for
various performance measures, we show Pareto-optimality of and give an
Occam's razor argument that the choice for the weights
is optimal, where is the length of the shortest program describing
. The results are applied to games of chance, defined as a sequence of
bets, observations, and rewards. The prediction schemes (and bounds) are
compared to the popular predictors based on expert advice. Extensions to
infinite alphabets, partial, delayed and probabilistic prediction,
classification, and more active systems are briefly discussed.Comment: 34 page
The Complexity of POMDPs with Long-run Average Objectives
We study the problem of approximation of optimal values in
partially-observable Markov decision processes (POMDPs) with long-run average
objectives. POMDPs are a standard model for dynamic systems with probabilistic
and nondeterministic behavior in uncertain environments. In long-run average
objectives rewards are associated with every transition of the POMDP and the
payoff is the long-run average of the rewards along the executions of the
POMDP. We establish strategy complexity and computational complexity results.
Our main result shows that finite-memory strategies suffice for approximation
of optimal values, and the related decision problem is recursively enumerable
complete
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