7,244 research outputs found
Deep Variational Reinforcement Learning for POMDPs
Many real-world sequential decision making problems are partially observable
by nature, and the environment model is typically unknown. Consequently, there
is great need for reinforcement learning methods that can tackle such problems
given only a stream of incomplete and noisy observations. In this paper, we
propose deep variational reinforcement learning (DVRL), which introduces an
inductive bias that allows an agent to learn a generative model of the
environment and perform inference in that model to effectively aggregate the
available information. We develop an n-step approximation to the evidence lower
bound (ELBO), allowing the model to be trained jointly with the policy. This
ensures that the latent state representation is suitable for the control task.
In experiments on Mountain Hike and flickering Atari we show that our method
outperforms previous approaches relying on recurrent neural networks to encode
the past
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Domain shifts in the training data are common in practical applications of
machine learning, they occur for instance when the data is coming from
different sources. Ideally, a ML model should work well independently of these
shifts, for example, by learning a domain-invariant representation. Moreover,
privacy concerns regarding the source also require a domain-invariant
representation. In this work, we provide theoretical results that link domain
invariant representations -- measured by the Wasserstein distance on the joint
distributions -- to a practical semi-supervised learning objective based on a
cross-entropy classifier and a novel domain critic. Quantitative experiments
demonstrate that the proposed approach is indeed able to practically learn such
an invariant representation (between two domains), and the latter also supports
models with higher predictive accuracy on both domains, comparing favorably to
existing techniques.Comment: 20 pages including appendix. Under Revie
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