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Safe Bayesian optimization
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Classroom Contents
Safe and Efficient Exploration in Reinforcement Learning
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- 1 Intro
- 2 RL beyond simulated environments?
- 3 Tuning the Swiss Free Electron Laser [with Kirschner, Muty, Hiller, Ischebeck et al.]
- 4 Challenge: Safety Constraints
- 5 Safe optimization
- 6 Safe Bayesian optimization
- 7 Illustration of Gaussian Process Inference [cf, Rasmussen & Williams 2006]
- 8 Plausible maximizers
- 9 Certifying Safety
- 10 Confidence intervals for GPS?
- 11 Online tuning of 24 parameters
- 12 Shortcomings of Safe Opt
- 13 Safe learning for dynamical systems Koller, Berkenkamp, Turchetta, K CDC 18, 19
- 14 Stylized task
- 15 Planning with confidence bounds Koller, Berkenkamp, Turchetta, K CDC 18, 19
- 16 Forwards-propagating uncertain, nonlinear dynamics
- 17 Challenges with long-term action dependencies
- 18 Safe learning-based MPC
- 19 Experimental illustration
- 20 Scaling up: Efficient Optimistic Exploration in Deep Model based Reinforcement Learning
- 21 Optimism in Model-based Deep RL
- 22 Deep Model-based RL with Confidence: H-UCRL [Curi, Berkenkamp, K, Neurips 20]
- 23 Illustration on Inverted Pendulum
- 24 Deep RL: Mujoco Half-Cheetah
- 25 Action penalty effect
- 26 What about safety?
- 27 Safety-Gym Benchmark Suite
- 28 Which priors to choose? → PAC-Bayesian Meta Learning [Rothfuss, Fortuin, Josifoski, K, ICML 2021]
- 29 Experiments - Predictive accuracy (Regression)
- 30 Meta-Learned Priors for Bayesian Optimization
- 31 Meta-Learned Priors for Sequential Decision Making
- 32 Safe and efficient exploration in real-world RL
- 33 Acknowledgments
