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Verifiably Safe Off-Model Reinforcement Learning
[chapter]
2019
Lecture Notes in Computer Science
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This paper introduces verification-preserving model updates, the first approach toward obtaining formal safety guarantees for reinforcement learning in settings where multiple possible environmental models ...
Acting well given an accurate environmental model is an important pre-requisite for safe learning, but is ultimately insufficient for systems that operate in complex heterogeneous environments. ...
We call this problem verifiably safe off-model learning. In this paper we introduce a first approach toward obtaining formal safety proofs for off-model learning. ...
doi:10.1007/978-3-030-17462-0_28
fatcat:h7tbnexlfrbl5lsc223tnjqary
Verifiably Safe Off-Model Reinforcement Learning
[article]
2019
arXiv
pre-print
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This paper introduces verification-preserving model updates, the first approach toward obtaining formal safety guarantees for reinforcement learning in settings where multiple environmental models must ...
Acting well given an accurate environmental model is an important pre-requisite for safe learning, but is ultimately insufficient for systems that operate in complex heterogeneous environments. ...
Our contributions enabling verifiably safe off-model learning include: 1. ...
arXiv:1902.05632v1
fatcat:b3celfznhfapfcr6r4zub6t75q
Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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by reinforcement learning. ...
Verification results are preserved whenever learning agents limit exploration within the confounds of verified control choices as long as observed reality comports with the model used for off-line verification ...
Justified Speculative Control combines verified runtime monitoring -backed by formally verified models -with reinforcement learning. ...
doi:10.1609/aaai.v32i1.12107
fatcat:ihbimfppzbakbna25vrmo6rcxu
Verified Probabilistic Policies for Deep Reinforcement Learning
[article]
2022
arXiv
pre-print
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In this paper, we tackle the problem of verifying probabilistic policies for deep reinforcement learning, which are used to, for example, tackle adversarial environments, break symmetries and manage trade-offs ...
There is also growing interest in formally verifying that such policies are correct and execute safely. ...
We show that our approach successfully verifies probabilistic policies trained for several reinforcement learning benchmarks and explore trade-offs in precision and computational efficiency. ...
arXiv:2201.03698v2
fatcat:dix7xgasfrewbjrqdygzgf65fu
Runtime Safety Assurance Using Reinforcement Learning
[article]
2020
arXiv
pre-print
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We frame the design of RTSA with the Markov decision process (MDP) framework and use reinforcement learning (RL) to solve it. ...
When the system is triggered, a verified recovery controller is deployed. ...
From these episodes, we learn the parameters in an offline approach known as batch reinforcement learning. ...
arXiv:2010.10618v1
fatcat:ndx4lwhwtreylhmgbjrbh5mp2a
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1904.07189v1 fatcat:zfhwlcfsobhutnofloz22kcd74
Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the performance of the resulting policy. ...
Reinforcement Learning In reinforcement learning (RL), the environment is modeled as an MDP with unknown transition and reward models (Kaelbling et al., 1996) . ...
A reinforcement learning algorithm to derive provably safe policies has been demonstrated for hybrid systems in (Fulton et al., 2018) . ...
arXiv:1904.07189v2
fatcat:fxsjjitdeven3a56jn6oxxohbm
Learning-Based Model Predictive Control for Safe Exploration
2018
2018 IEEE Conference on Decision and Control (CDC)
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We combine a provably safe learning-based MPC scheme that allows for input-dependent uncertainties with techniques from model-based RL to solve tasks with only limited prior knowledge. ...
Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. ...
SAFE REINFORCEMENT LEARNING We design a MPC scheme that can solve a given RL task under safety constraints. ...
doi:10.1109/cdc.2018.8619572
dblp:conf/cdc/KollerBT018
fatcat:omofexgb6vbzrnuluspinjmnmu
Safe adaptation in multiagent competition
[article]
2022
arXiv
pre-print
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To overcome this difficulty, we developed a safe adaptation approach in which the ego-agent is trained against a regularized opponent model, which effectively avoids overfitting and consequently improves ...
In multiagent competitive scenarios, agents may have to adapt to new opponents with previously unseen behaviors by learning from the interaction experiences between the ego-agent and the opponent. ...
reinforcement learning setting. ...
arXiv:2203.07562v1
fatcat:jc23hg567jerboxajlqcaz74pu
Verifiably Safe Exploration for End-to-End Reinforcement Learning
[article]
2020
arXiv
pre-print
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Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. ...
Our benchmark draws from several proposed problem sets for safe learning and includes problems that emphasize challenges such as reward signals that are not aligned with safety constraints. ...
Supplementary material for: Verifiably Safe Exploration for End-to-End Reinforcement Learning A Model Monitoring We use differential Dynamic Logic (dL) (Platzer, 2008 (Platzer, , 2010 (Platzer, , 2012 ...
arXiv:2007.01223v1
fatcat:shyzxdco5jhzlfwpefvf2ejnly
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding
[article]
2021
arXiv
pre-print
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In recent years, a variety of approaches have been put forward to address the challenges of safety-aware reinforcement learning; however, these methods often either require a handcrafted model of the environment ...
We present a novel approach to safety-aware deep reinforcement learning in high-dimensional environments called latent shielding. ...
Symbolic Reinforcement Learning for Safe RAN ...
arXiv:2112.11490v1
fatcat:o5curea7ena5blafok3uldvfni
Safe Q-Learning Method Based on Constrained Markov Decision Processes
2019
IEEE Access
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The experiments verify the effectiveness of the algorithm. INDEX TERMS Constrained Markov decision processes, safe reinforcement learning, Q-learning, constraint, Lagrange multiplier. ...
In order to solve the aforementioned problem, we come forward with a safe Q-learning method that is based on constrained Markov decision processes, adding safety constraints as prerequisites to the model ...
Safe Q-learning, however, can solve model-free problem for Q-learning is an efficient model-free reinforcement algorithm. ...
doi:10.1109/access.2019.2952651
fatcat:wzezmhbadbabtjoy47vxpjgqdu
Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles
[article]
2022
arXiv
pre-print
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While the previous studies synthesize simulators and model-free DRL to reduce online computation, this work proposes a Safe Off-policy Model-Based Reinforcement Learning algorithm for the eco-driving problem ...
First, the combination of off-policy learning and the use of a physics-based model improves the sample efficiency. ...
The second contribution of this work, from the RL algorithm perspective, is the development of Safe Model-based Off-policy Reinforcement Learning (SMORL), a safe-critical model-based off-policy Q-learning ...
arXiv:2105.11640v2
fatcat:6nj7e5vej5b3rkfp4snjk7nt4e
Fuzzy controller, designed by reinforcement learning, for vehicle traction system application
2021
Mathematical Modeling and Computing
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To verify the performance of the proposed controller, the adaptive fuzzy controller tuned by reinforcement learning is applied to the mathematical model of a wheel locomotion module of an electric vehicle ...
Thus, the designed fuzzy control that is tuned by reinforcement learning is capable to ensure the stable, optimal, and safe performance of the system and takes into account external disturbances. ...
Background
Reinforcement learning: Q-learning algorithm Reinforcement learning is a subfield of machine learning. ...
doi:10.23939/mmc2021.02.168
fatcat:j52xso7ejja5rktkmq3po7gpte
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
[article]
2022
arXiv
pre-print
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//github.com/chauncygu/Safe-Reinforcement-Learning-Baselines.git. ...
Reinforcement learning (RL) has achieved tremendous success in many complex decision making tasks. ...
Model-Based Safe Reinforcement Learning Linear program and Lagrangian approximation are widely used in modelbased safe reinforcement learning if the estimated transition model is either given or estimated ...
arXiv:2205.10330v3
fatcat:2xcflmxcffeejdsrcumnvx6x2e
Near Optimal Control With Reachability and Safety Guarantees
2019
IFAC-PapersOnLine
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We propose a method to construct a near-optimal control law by means of model-based reinforcement learning and subsequently verifying the reachability and safety of the closed-loop control system through ...
We propose a method to construct a near-optimal control law by means of model-based reinforcement learning and subsequently verifying the reachability and safety of the closed-loop control system through ...
We propose a method to construct a near-optimal control law by means of model-based reinforcement learning and subsequently verifying the reachability and safety of the closed-loop control system through ...
doi:10.1016/j.ifacol.2019.09.146
fatcat:465axkqg5bb6hhvxb7xmyp2ik4
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