53 citations · 157 across the 21 of their papers we have counts for
32 papers
Structured Q-learning For Antibody Design
Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla +5
Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences. For example, one of the crucial steps involved in antibody desi…
Effects of Safety State Augmentation on Safe Exploration
Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang +1
Safe exploration is a challenging and important problem in model-free reinforcement learning (RL). Often the safety cost is sparse and unknown, which unavoidably leads to constrain…
SEREN: Knowing When to Explore and When to Exploit
Changmin Yu, David Mguni, Dong Li +3
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To enc…
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
David Mguni, Aivar Sootla, Juliusz Ziomek +4
Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at…
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
Hang Ren, Aivar Sootla, Taher Jafferjee +3
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 (disconti…
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee +4
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plan…