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Sample-efficient and Scalable Exploration in Continuous-Time RL
arXiv – CS AI|Klemens Iten, Lenart Treven, Bhavya Sukhija, Florian D\"orfler, Andreas Krause||4 views
🤖AI Summary
Researchers introduce COMBRL, a new reinforcement learning algorithm designed for continuous-time systems using nonlinear ordinary differential equations. The algorithm achieves sublinear regret and better sample efficiency compared to existing methods by combining probabilistic models with uncertainty-aware exploration.
Key Takeaways
- →COMBRL addresses the gap between discrete-time RL algorithms and continuous-time real-world control systems
- →The algorithm uses Gaussian processes and Bayesian neural networks to learn uncertainty-aware ODE models
- →COMBRL achieves sublinear regret bounds in reward-driven settings and provides sample complexity bounds for unsupervised RL
- →Experimental results show improved scalability and sample efficiency compared to baseline methods
- →The approach works in both standard RL and unsupervised RL settings without extrinsic rewards
#reinforcement-learning#continuous-time#machine-learning#ode#bayesian-neural-networks#gaussian-processes#model-based-rl#sample-efficiency
Read Original →via arXiv – CS AI
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