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Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Deep Reinforcement Learning (DRL) has achieved impressive performance in...
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Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems
Learning controllers merely based on a performance metric has been prove...
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Soft Q-network
When DQN is announced by deepmind in 2013, the whole world is surprised ...
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Complete stability analysis of a heuristic ADP control design
This paper provides new stability results for Action-Dependent Heuristic...
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Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control
Reinforcement Learning (RL) algorithms have achieved remarkable performa...
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Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulation
Reinforcement learning (RL) has had its fair share of success in contact...
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Policy Optimization for H_2 Linear Control with H_∞ Robustness Guarantee: Implicit Regularization and Global Convergence
Policy optimization (PO) is a key ingredient for reinforcement learning ...
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Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data evaluated by certain performance criteria through trial-and-error. However, the data-based learning approach is notorious for not guaranteeing stability, which is the most fundamental property for any control system. In this paper, the classic Lyapunov's method is explored to analyze the uniformly ultimate boundedness stability (UUB) solely based on data without using a mathematical model. It is further shown how RL with UUB guarantee can be applied to control dynamic systems with safety constraints. Based on the theoretical results, both off-policy and on-policy learning algorithms are proposed respectively. As a result, optimal controllers can be learned to guarantee UUB of the closed-loop system both at convergence and during learning. The proposed algorithms are evaluated on a series of robotic continuous control tasks with safety constraints. In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety. As a qualitative evaluation of stability, our method shows impressive resilience even in the presence of external disturbances.
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