Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems. However, RL algorithms are sample inefficient and require a large number of interaction with the environment to synthesize optimal control strategies. Consequently, applications of RL are typically limited to sparse sensors and actuators due to the curse of dimensionality entailed by the exploration-exploitation dilemma in high-dimensional spaces. In this work, we bridge RL and traditional optimal control for dynamical system with a novel Physics-EnhAnced Reinf
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- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “Physics-enhanced reinforcement learning for real-time optima” ≈ “lllyasviel/ControlNet””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “Physics-enhanced reinforcement learning for real-time optima” ≈ “lllyasviel/ControlNet-v1-1””
- PossiblePossibly related (embedding) · 48%Gradient-based Planning for World Models at Longer Horizons →
- PossiblePossibly related (embedding) · 46%Autonomous navigation of intelligent microrobotic swarms in unknown environments →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Physics-enhanced reinforcement learning for real-time optima” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 59%builderz-labs/mission-control →
“Fuzzy title match (0.73): “Physics-enhanced reinforcement learning for real-time optima” ≈ “builderz-labs/mission-control””
- LinkedLinked via arxiv author · 85%Matteo Tomasetto →
“Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems”
- LinkedLinked via arxiv author · 85%Nicolò Botteghi →
“Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems”