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A Review of Safe Reinforcement Learning: Methods, Theory and Applications
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Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world applications, leading to a growing demand for safe RL algorithms, such as in autonomous driving and robotics scenarios. While safe control has a long history, the study of safe RL algorithms is still in the early stages. To establish a good foundation for future safe RL research, in this paper, we provide a review of safe RL from the perspectives of methods, theories, and applications. Firstly, we review the progress of safe RL from five dimensions and come up with five crucial problems for safe RL being deployed in real-world applications, coined as "2H3W". Secondly, we analyze the algorithm and theory progress from the perspectives of answering the "2H3W" problems. Particularly, the sample complexity of safe RL algorithms is reviewed and discussed, followed by an introduction to the applications and benchmarks of safe RL algorithms. Finally, we open the discussion of the challenging problems in safe RL, hoping to inspire future research on this thread. To advance the study of safe RL algorithms, we release an open-sourced repository containing the implementations of major safe RL algorithms at the link: https://github.com/chauncygu/Safe-Reinforcement-Learning-Baselines.git.
Forward citations
Cited by 11 Pith papers
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Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning
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Safe Planning and Policy Optimization via World Model Learning
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SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems
SafeOR-Gym offers nine constrained OR environments for safe RL and shows that existing algorithms solve some but fail on mixed-integer or nonconvex instances.
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Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning
A DP-based certified defense provides lower bounds on expected cumulative reward and per-state action stability for offline RL under transition- and trajectory-level poisoning, with larger certified radii than COPA.
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End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
An end-to-end humanoid locomotion policy maps raw LiDAR point clouds to motor commands using P3O with CBF-inspired safety costs and comfort rewards, with sim-to-real tests on a Unitree G1.
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HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving
The HCRMP planner feeds LLM semantic hints into state representation and critic weighting instead of letting the LLM decide actions, reporting better CARLA driving metrics.
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Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning
An adaptive Koopman MPC with historical safety constraints is proposed for tobacco conditioning, but the claimed Cpk improvements come from model-generated advisor-mode trajectories, not real closed-loop control.
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Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies
A single-layer Lipschitz bound for pruning is correct, but the paper's additive multi-layer bound is false and the control-safety framing is unsupported.
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Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact
A broad survey arguing that AGI requires modular, memory-augmented, embodied architectures rather than scaled-up token prediction, with a brief proposal to decompose intelligence into five components.
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