Adaptive temporal-difference learning via deep neural network function approximation: a non-asymptotic analysis
Abstract Although deep reinforcement learning has achieved notable practical achievements, its theoretical foundations have been scarcely explored until recent times. Nonetheless, the rate of convergence for current neural temporal-difference (TD) learning algorithms is constrained, largely due to t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01757-w |
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