Search Results for author: Marc Rigter

Found 11 papers, 6 papers with code

World Models via Policy-Guided Trajectory Diffusion

1 code implementation13 Dec 2023 Marc Rigter, Jun Yamada, Ingmar Posner

Our results demonstrate that PolyGRAD outperforms state-of-the-art baselines in terms of trajectory prediction error for short trajectories, with the exception of autoregressive diffusion.

Continuous Control Denoising +2

TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer

no code implementations7 Nov 2023 Jun Yamada, Marc Rigter, Jack Collins, Ingmar Posner

The teacher world model then supervises a student world model that takes the domain-randomised image observations as input.

A Framework for Learning from Demonstration with Minimal Human Effort

1 code implementation15 Jun 2023 Marc Rigter, Bruno Lacerda, Nick Hawes

In this setting we address reinforcement learning, and learning from demonstration, where there is a cost associated with human time.

reinforcement-learning

Reward-Free Curricula for Training Robust World Models

1 code implementation15 Jun 2023 Marc Rigter, Minqi Jiang, Ingmar Posner

We consider robustness in terms of minimax regret over all environment instantiations and show that the minimax regret can be connected to minimising the maximum error in the world model across environment instances.

Risk-Sensitive and Robust Model-Based Reinforcement Learning and Planning

no code implementations2 Apr 2023 Marc Rigter

The over-arching goal of this thesis is to contribute to developing algorithms that mitigate both sources of uncertainty in sequential decision-making problems.

Decision Making Model-based Reinforcement Learning +3

RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

2 code implementations26 Apr 2022 Marc Rigter, Bruno Lacerda, Nick Hawes

Model-based algorithms, which learn a model of the environment from the dataset and perform conservative policy optimisation within that model, have emerged as a promising approach to this problem.

Offline RL reinforcement-learning +1

Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction

no code implementations14 Mar 2022 Marc Rigter, Danial Dervovic, Parisa Hassanzadeh, Jason Long, Parisa Zehtabi, Daniele Magazzeni

To improve the scalability of our approach to a greater number of task classes, we present an approximation based on state abstraction.

Planning for Risk-Aversion and Expected Value in MDPs

1 code implementation25 Oct 2021 Marc Rigter, Paul Duckworth, Bruno Lacerda, Nick Hawes

This motivates us to propose a lexicographic approach which minimises the expected cost subject to the constraint that the CVaR of the total cost is optimal.

Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes

no code implementations8 Dec 2020 Marc Rigter, Bruno Lacerda, Nick Hawes

We propose a dynamic programming algorithm that utilises the regret Bellman equation, and show that it optimises minimax regret exactly for UMDPs with independent uncertainties.

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