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Found 3 papers, 1 papers with code
On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR
Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data usage remains a central challenge to be tackled for its successful real-world applications.
Anderson Acceleration for Partially Observable Markov Decision Processes: A Maximum Entropy Approach
Inspired by the quasi-Newton interpretation of AA, we propose a maximum entropy variant of QMDP, which we call soft QMDP, to fully benefit from AA.
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls.
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