4 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems
Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli
We study rational synthesis for stochastic multi-agent systems with unknown dynamics, where each agent is equipped with its own probabilistic temporal objective specified using Pro…
Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning
Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli
Safe coordination problems surface in multi-agent reinforcement learning when global safety cannot be enforced by any agent unilaterally: the admissibility of one agent's action ma…
SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
Maksim Anisimov, Francesco Belardinelli, Matthew Wicker
Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments exhibit non-stationary dynamic…
Expressive Temporal Specifications for Reward Monitoring
Omar Adalat, Francesco Belardinelli
Specifying informative and dense reward functions remains a pivotal challenge in Reinforcement Learning, as it directly affects the efficiency of agent training. In this work, we h…
Expressive Reward Synthesis with the Runtime Monitoring Language
Daniel Donnelly, Angelo Ferrando, Francesco Belardinelli
A key challenge in reinforcement learning (RL) is reward (mis)specification, whereby imprecisely defined reward functions can result in unintended, possibly harmful, behaviours. In…
Explainable Reinforcement Learning for Formula One Race Strategy
Devin Thomas, Junqi Jiang, Avinash Kori +6
In Formula One, teams compete to develop their cars and achieve the highest possible finishing position in each race. During a race, however, teams are unable to alter the car, so…