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 implementationsLatest papers
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.
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).
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.
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.
Safe Policy Optimization with Local Generalized Linear Function Approximations
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems.
Infinite Time Horizon Safety of Bayesian Neural Networks
mlech26l/bayesian_nn_safety
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NeurIPS 2021
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction.
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic.
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
zlr20/saferl_kit
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The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.
Safe Continuous Control with Constrained Model-Based Policy Optimization
Further, we provide theoretical and empirical analyses regarding the implications of model-usage on constrained policy optimization problems and introduce a practical algorithm that accelerates policy search with model-generated data.
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap
Autonomous exploration requires robots to generate informative trajectories iteratively.
