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
Benchmarks
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Libraries
Use these libraries to find Safe Exploration models and implementationsMost implemented papers
Safe Policy Optimization with Local Generalized Linear Function Approximations
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems.
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations.
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage.
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
rl-boxes/safe-rl
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Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE).
Effects of Safety State Augmentation on Safe Exploration
huawei-noah/hebo
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We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks
Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks.
Safe Exploration Method for Reinforcement Learning under Existence of Disturbance
fujitsuresearch/safeexploration
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We define the safety during learning as satisfaction of the constraint conditions explicitly defined in terms of the state and propose a safe exploration method that uses partial prior knowledge of a controlled object and disturbance.
Near-Optimal Multi-Agent Learning for Safe Coverage Control
In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety.
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
akjayant/mbppol
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We compare our approach with relevant model-free and model-based approaches in Constrained RL using the challenging Safe Reinforcement Learning benchmark - the Open AI Safety Gym.
Atlas: Automate Online Service Configuration in Network Slicing
int-unl/atlas
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First, we design a learning-based simulator to reduce the sim-to-real discrepancy, which is accomplished by a new parameter searching method based on Bayesian optimization.
