19 citations · 25 across the 8 of their papers we have counts for
8 papers
A Framework for Learning from Demonstration with Minimal Human Effort
Marc Rigter, Bruno Lacerda, Nick Hawes
We consider robot learning in the context of shared autonomy, where control of the system can switch between a human teleoperator and autonomous control. In this setting we address…
Risk-Sensitive and Robust Model-Based Reinforcement Learning and Planning
Marc Rigter
Many sequential decision-making problems that are currently automated, such as those in manufacturing or recommender systems, operate in an environment where there is either little…
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Offline reinforcement learning (RL) is suitable for safety-critical domains where online exploration is too costly or dangerous. In such safety-critical settings, decision-making s…
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Offline reinforcement learning (RL) aims to find performant policies from logged data without further environment interaction. Model-based algorithms, which learn a model of the en…
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
Marc Rigter, Danial Dervovic, Parisa Hassanzadeh +3
We consider a novel queuing problem where the decision-maker must choose to accept or reject randomly arriving tasks into a no buffer queue which are processed by identical ser…
Planning for Risk-Aversion and Expected Value in MDPs
Marc Rigter, Paul Duckworth, Bruno Lacerda +1
Planning in Markov decision processes (MDPs) typically optimises the expected cost. However, optimising the expectation does not consider the risk that for any given run of the MDP…
Risk-Averse Bayes-Adaptive Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adap…
Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes
Marc Rigter, Bruno Lacerda, Nick Hawes
The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets which the parameters belo…