Search Results for author:
Found 13 papers, 7 papers with code
Monte Carlo Tree Search with Boltzmann Exploration
Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques.
JaxMARL: Multi-Agent RL Environments in JAX
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
A Framework for Learning from Demonstration with Minimal Human Effort
In this setting we address reinforcement learning, and learning from demonstration, where there is a cost associated with human time.
Formal Modelling for Multi-Robot Systems Under Uncertainty
Purpose of Review: To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution.
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Our model-based approach is risk-averse to both epistemic and aleatoric uncertainty.
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
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.
Planning for Risk-Aversion and Expected Value in MDPs
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.
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference
Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning.
Risk-Averse Bayes-Adaptive Reinforcement Learning
In this work, we address risk-averse Bayes-adaptive reinforcement learning.
Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes
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.
Active Inference for Integrated State-Estimation, Control, and Learning
This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators.
Robotics
Convex Hull Monte-Carlo Tree Search
This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives.
Simultaneous Task Allocation and Planning Under Uncertainty
We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments.
