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Found 12 papers, 3 papers with code
Structured Q-learning For Antibody Design
Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences.
Effects of Safety State Augmentation on Safe Exploration
We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
In this paper, we introduce a reinforcement learning (RL) framework named \textbf{L}earnable \textbf{I}mpulse \textbf{C}ontrol \textbf{R}einforcement \textbf{A}lgorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs.
SEREN: Knowing When to Explore and When to Exploit
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states.
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.
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.
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available.
Diagnosing and Preventing Instabilities in Recurrent Video Processing
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution.
SAMBA: Safe Model-Based & Active Reinforcement Learning
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach
In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks.
Toggling a Genetic Switch Using Reinforcement Learning
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks.
