Research map / Reinforcement learning / MDPs and planning
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sufficiently overparameterized deep ReLU function approximation for Q-learning with Markovian, non-i.i.d. data | Neural Q-learning · MDP with non-i.i.d. observations | convergence rate: O(1/√T) | Helped |
| Increase network width so the projection error decreases | Neural Q-learning · MDP with non-i.i.d. observations | mean squared Q-value error: 1/m^{1/6} | Helped |
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