4 research outputs found
Adaptive Informative Path Planning with Multimodal Sensing
Adaptive Informative Path Planning (AIPP) problems model an agent tasked with
obtaining information subject to resource constraints in unknown, partially
observable environments. Existing work on AIPP has focused on representing
observations about the world as a result of agent movement. We formulate the
more general setting where the agent may choose between different sensors at
the cost of some energy, in addition to traversing the environment to gather
information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS
requires reasoning jointly about the effects of sensing and movement in terms
of both energy expended and information gained. We frame AIPPMS as a Partially
Observable Markov Decision Process (POMDP) and solve it with online planning.
Our approach is based on the Partially Observable Monte Carlo Planning
framework with modifications to ensure constraint feasibility and a heuristic
rollout policy tailored for AIPPMS. We evaluate our method on two domains: a
simulated search-and-rescue scenario and a challenging extension to the classic
RockSample problem. We find that our approach outperforms a classic AIPP
algorithm that is modified for AIPPMS, as well as online planning using a
random rollout policy.Comment: First two authors contributed equally; International Conference on
Automated Planning and Scheduling (ICAPS) 202
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Bayesian optimization (BayesOpt) is a gold standard for query-efficient
continuous optimization. However, its adoption for drug design has been
hindered by the discrete, high-dimensional nature of the decision variables. We
develop a new approach (LaMBO) which jointly trains a denoising autoencoder
with a discriminative multi-task Gaussian process head, allowing gradient-based
optimization of multi-objective acquisition functions in the latent space of
the autoencoder. These acquisition functions allow LaMBO to balance the
explore-exploit tradeoff over multiple design rounds, and to balance objective
tradeoffs by optimizing sequences at many different points on the Pareto
frontier. We evaluate LaMBO on two small-molecule design tasks, and introduce
new tasks optimizing \emph{in silico} and \emph{in vitro} properties of
large-molecule fluorescent proteins. In our experiments LaMBO outperforms
genetic optimizers and does not require a large pretraining corpus,
demonstrating that BayesOpt is practical and effective for biological sequence
design.Comment: ICML 2022. Code available at https://github.com/samuelstanton/lamb