Search Results for author:
Found 10 papers, 3 papers with code
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution.
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations
The Schr\"odinger equation is at the heart of modern quantum mechanics.
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Therefore, it is a priority to design optimal antigen-specific CDRH3 regions to develop therapeutic antibodies to combat harmful pathogens.
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
Compositional ADAM: An Adaptive Compositional Solver
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.
Learning High-level Representations from Demonstrations
A common approach to HL, is to provide the agent with a number of high-level skills that solve small parts of the overall problem.
Balancing Two-Player Stochastic Games with Soft Q-Learning
Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers.
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly.
