Safe Exploration
35 papers with code • 0 benchmarks • 0 datasets
Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.
Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
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Use these libraries to find Safe Exploration models and implementationsLatest papers with no code
Information-Theoretic Safe Bayesian Optimization
In this paper, we propose an information-theoretic safe exploration criterion that directly exploits the GP posterior to identify the most informative safe parameters to evaluate.
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL.
Towards Socially and Morally Aware RL agent: Reward Design With LLM
When we design and deploy an Reinforcement Learning (RL) agent, reward functions motivates agents to achieve an objective.
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning
Taking this problem into account, we propose a safe learning-based load balancing algorithm for Software Defined-Wide Area Network (SD-WAN), which is empowered by Deep Reinforcement Learning (DRL) combined with a Control Barrier Function (CBF).
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations
This framework leverages RL to learn a better backup policy to enlarge the forward invariant set, while guaranteeing safety during training.
Safe Reinforcement Learning in a Simulated Robotic Arm
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies.
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
We then propose a solution of the GSE problem in the form of a meta-algorithm for safe exploration, MASE, which combines an unconstrained RL algorithm with an uncertainty quantifier to guarantee safety in the current episode while properly penalizing unsafe explorations before actual safety violation to discourage them in future episodes.
Reinforcement Learning by Guided Safe Exploration
Drawing from transfer learning, we also regularize a target policy (the student) towards the guide while the student is unreliable and gradually eliminate the influence of the guide as training progresses.
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)
We demonstrate our method's effectiveness in reducing safety violations during online exploration in preliminary experiments by an average of 40. 3% compared with QL and DQN standard algorithms and 29. 1% compared with previous related work, while achieving comparable cumulative rewards with respect to unrestricted exploration and alternative approaches.
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer.
