Safe Reinforcement Learning

64 papers with code • 0 benchmarks • 1 datasets

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Use these libraries to find Safe Reinforcement Learning models and implementations
3 papers
3 papers

Most implemented papers

Constrained Policy Optimization

jachiam/cpo ICML 2017

For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function.

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

utiasDSL/safe-control-gym 13 Aug 2021

The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities.

Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

mahaitongdae/Feasible-Actor-Critic 22 May 2021

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.

Datasets and Benchmarks for Offline Safe Reinforcement Learning

liuzuxin/osrl 15 Jun 2023

This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.

Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments

sisl/AutomotiveSafeRL 25 Apr 2019

Navigating urban environments represents a complex task for automated vehicles.

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

zlr20/saferl_kit 29 Oct 2020

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

decisionforce/metadrive 26 Sep 2021

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.

Reachability Constrained Reinforcement Learning

mahaitongdae/Reachability_Constrained_RL 16 May 2022

Recent studies incorporate feasible sets into CRL with energy-based methods such as control barrier function (CBF), safety index (SI), and leverage prior conservative estimations of feasible sets, which harms the performance of the learned policy.

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning

lasgroup/lbsgd-rl 21 Jul 2022

We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial.

Constrained Update Projection Approach to Safe Policy Optimization

rl-boxes/safe-rl 15 Sep 2022

Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance.