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Found 11 papers, 5 papers with code
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions.
Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers.
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
In this work, we propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs to provide a graphical representation of the sub-goals to a reinforcement learning (RL) agent that does not have access to the transition dynamics of the environment.
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
We use Generative Adversarial Networks (GANs) along with a cycle-consistency loss to map the observations between the source and target domains and later use this learned mapping to clone the successful source task behavior policy to the target domain.
Novelty Detection in Reinforcement Learning with World Models
We refer to the sudden change in visual properties or state transitions as novelties.
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e. g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e. g, Q-Learning for Reward Machines).
Neuro-Symbolic World Models for Adapting to Open World Novelty
Additionally, WorldCloner augments the policy learning process using imagination-based adaptation, where the world model simulates transitions of the post-novelty environment to help the policy adapt.
Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation.
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
We provide an ontology of for novelties most relevant to sequential decision making, which distinguishes between novelties that affect objects versus actions, unary properties versus non-unary relations, and the distribution of solutions to a task.
Diverse Exploration for Fast and Safe Policy Improvement
We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains.
Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors
To estimate the health effects of complex mixtures we propose a flexible Bayesian approach that allows exposures to interact with each other and have nonlinear relationships with the outcome.
Methodology
