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Found 5 papers, 2 papers with code
Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific.
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
We propose a dynamic programming-based method to efficiently solve for the optimal growth value function when true currents are known.
Safe Connectivity Maintenance in Underactuated Multi-Agent Networks for Dynamic Oceanic Environments
To address these challenges, we propose a Hierarchical Multi-Agent Control approach that allows arbitrary single agent performance policies that are unaware of other agents to be used in multi-agent systems, while ensuring safe operation.
Inducing Structure in Reward Learning by Learning Features
To get around this issue, recent deep Inverse Reinforcement Learning (IRL) methods learn rewards directly from the raw state but this is challenging because the robot has to implicitly learn the features that are important and how to combine them, simultaneously.
Feature Expansive Reward Learning: Rethinking Human Input
When the correction cannot be explained by these features, recent work in deep Inverse Reinforcement Learning (IRL) suggests that the robot could ask for task demonstrations and recover a reward defined over the raw state space.
