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Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration.
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Differential dynamic logic for hybrid systems
Platzer, A · 2008
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Logical Analysis of Hybrid Systems: Proving Theorems for Complex Dynamics
Platzer, A · 2010
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International Organization for Standardization 26262 road vehicles – functional safety
ISO-26262 · 2011
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Platzer, A · 2012
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On provably safe obstacle avoidance for autonomous robotic ground vehicles
Mitsch, S., Ghorbal, K., and Platzer, A · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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KeYmaera X: An axiomatic tactical theorem prover for hybrid systems
Fulton, N., Mitsch, S., Quesel, J.-D., Völp, M., and Platzer, A · 2015
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A comprehensive survey on safe reinforcement learning
Garcıa, J. and Fernández, F · 2015
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A uniform substitution calculus for differential dynamic logic
Platzer, A · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M. I., and Moritz, P · 2015
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Towards deep symbolic reinforcement learning
Garnelo, M., Arulkumaran, K., and Shanahan, M · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?
Kalra, N. and Paddock, S. M · 2016
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ModelPlex: Verified runtime validation of verified cyber-physical system models
Mitsch, S. and Platzer, A · 2016
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How to model and prove hybrid systems with KeYmaera: a tutorial on safety
Quesel, J., Mitsch, S., Loos, S. M., Arechiga, N., and Platzer, A · 2016
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Constrained policy optimization
Achiam, J., Held, D., Tamar, A., and Abbeel, P · 2017
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Safe model-based reinforcement learning with stability guarantees
Berkenkamp, F., Turchetta, M., Schoellig, A., and Krause, A · 2017
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Bellerophon: Tactical theorem proving for hybrid systems
Fulton, N., Mitsch, S., Bohrer, B., and Platzer, A · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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A complete uniform substitution calculus for differential dynamic logic
Platzer, A · 2017
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Task-relevant object discovery and categorization for playing first-person shooter games
Liang, J. and Boularias, A · 2018
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Robot representing and reasoning with knowledge from reinforcement learning
Lu, K., Zhang, S., Stone, P., and Chen, X · 2018
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Verification for machine learning, autonomy, and neural networks survey
Xiang, W., Musau, P., Wild, A. A., Lopez, D. M., Hamilton, N., Yang, X., Rosenfeld, J., and Johnson, T. T · 2018
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Yang, F., Lyu, D., Liu, B., and Gustafson, S · 2018
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End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Safe reinforcement learning via shielding
Alshiekh, M., Bloem, R., Ehlers, R., Könighofer, B., Niekum, S., and Topcu, U · 2018
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Handbook of Model Checking
Clarke, E. M., Henzinger, T. A., Veith, H., and Bloem, R. (eds.) · 2018
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Safe exploration in continuous action spaces
Dalal, G., Dvijotham, K., Vecerik, M., Hester, T., Paduraru, C., and Tassa, Y · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., et al · 2018
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Safe reinforcement learning via formal methods: Toward safe control through proof and learning
Fulton, N. and Platzer, A · 2018
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Cheng, R., Orosz, G., Murray, R. M., and Burdick, J. W · 2019
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Foundations for restraining bolts: Reinforcement learning with ltlf/ldlf restraining specifications
De Giacomo, G., Iocchi, L., Favorito, M., and Patrizi, F · 2019
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Verifiably safe off-model reinforcement learning
Fulton, N. and Platzer, A · 2019
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Omega-regular objectives in model-free reinforcement learning
Hahn, E. M., Perez, M., Schewe, S., Somenzi, F., Trivedi, A., and Wojtczak, D · 2019
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Hasanbeig, M., Kantaros, Y., Abate, A. r., Kroening, D., Pappas, G. J., and Lee, I · 2019
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SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning
Lyu, D., Yang, F., Liu, B., and Gustafson, S · 2019
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Neural simplex architecture
Phan, D., Paoletti, N., Grosu, R., Jansen, N., Smolka, S. A., and Stoller, S. D · 2019
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Benchmarking Safe Exploration in Deep Reinforcement Learning
Ray, A., Achiam, J., and Amodei, D · 2019
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rlpyt: A research code base for deep reinforcement learning in pytorch, 2019
Stooke, A. and Abbeel, P · 2019
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Program search for machine learning pipelines leveraging symbolic planning and reinforcement learning
Yang, F., Gustafson, S., Elkholy, A., Lyu, D., and Liu, B · 2019
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Zhou, X., Wang, D., and Krähenbühl, P · 2019
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Cautious reinforcement learning with logical constraints
Hasanbeig, M., Abate, A., and Kroening, D · 2020
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