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Found 7 papers, 2 papers with code
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks.
Contrastive Example-Based Control
In this paper, we propose a method for offline, example-based control that learns an implicit model of multi-step transitions, rather than a reward function.
Train Offline, Test Online: A Real Robot Learning Benchmark
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data.
Autonomous Attack Mitigation for Industrial Control Systems
The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network.
Interpretable Local Tree Surrogate Policies
In this work, we propose a method to build predictable policy trees as surrogates for policies such as neural networks.
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration
Defending computer networks from cyber attack requires coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations.
Obstacle Avoidance Using a Monocular Camera
In this work, we present an obstacle avoidance system for small UAVs that uses a monocular camera with a hybrid neural network and path planner controller.
