24,796 research outputs found
Bayesian multitask inverse reinforcement learning
We generalise the problem of inverse reinforcement learning to multiple
tasks, from multiple demonstrations. Each one may represent one expert trying
to solve a different task, or as different experts trying to solve the same
task. Our main contribution is to formalise the problem as statistical
preference elicitation, via a number of structured priors, whose form captures
our biases about the relatedness of different tasks or expert policies. In
doing so, we introduce a prior on policy optimality, which is more natural to
specify. We show that our framework allows us not only to learn to efficiently
from multiple experts but to also effectively differentiate between the goals
of each. Possible applications include analysing the intrinsic motivations of
subjects in behavioural experiments and learning from multiple teachers.Comment: Corrected version. 13 pages, 8 figure
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ask whether it is feasible and truly beneficial to
combine rich probabilistic models with a closer approximation to fully Bayesian
planning. First, we use a collection of counterexamples to show formal problems
with the over-optimism inherent in Thompson sampling. Then we leverage
state-of-the-art techniques in efficient Bayes-adaptive planning and
non-parametric Bayesian methods to perform qualitatively better than both
existing conventional algorithms and Thompson sampling on two contextual
bandit-like problems.Comment: 11 pages, 11 figure
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