Pages that link to "Item:Q4899127"
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The following pages link to Safe Exploration of State and Action Spaces in Reinforcement Learning (Q4899127):
Displaying 13 items.
- Probabilistic inference for determining options in reinforcement learning (Q331688) (← links)
- Safe navigation in adversarial environments (Q723641) (← links)
- k-Certainty Exploration Method: an action selector to identify the environment in reinforcement learning (Q1392105) (← links)
- Reinforcement learning endowed with safe veto policies to learn the control of linked-multicomponent robotic systems (Q1749908) (← links)
- Risk-averse autonomous systems: a brief history and recent developments from the perspective of optimal control (Q2082497) (← links)
- Safe exploration in model-based reinforcement learning using control barrier functions (Q2103658) (← links)
- Safe global optimization of expensive noisy black-box functions in the \(\delta \)-Lipschitz framework (Q2156906) (← links)
- An active exploration method for data efficient reinforcement learning (Q2299097) (← links)
- Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics (Q6053801) (← links)
- Risk-aware controller for autonomous vehicles using model-based collision prediction and reinforcement learning (Q6103673) (← links)
- Explicit explore, exploit, or escape \((E^4)\): near-optimal safety-constrained reinforcement learning in polynomial time (Q6106432) (← links)
- Certified reinforcement learning with logic guidance (Q6136089) (← links)
- Probabilistic counterexample guidance for safer reinforcement learning (Q6546466) (← links)
