How learning rate for complex condition q-learning?
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The learning rate in Q-learning plays a crucial role in determining the algorithm's convergence rate and performance under various conditions. Different learning rate strategies impact the convergence behavior differently. For instance, a polynomial learning rate with a specific form can lead to a polynomial convergence rate in terms of the discount factor γ, while a linear learning rate may result in an exponential dependence on 1/(1-γ). Moreover, the choice of learning rate can significantly affect the speed and accuracy of Q-learning algorithms. Adaptive learning rate strategies, such as the ALR-KF Q-learning algorithm, have been proposed to dynamically adjust the learning rate based on the environmental rewards and cognitive uncertainty, leading to improved learning efficiency and accuracy.
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| Papers (4) | Insight |
|---|---|
Open access•Proceedings Article 01 Dec 1997 | The learning rate for Q-learning in complex conditions is O(1/tR(1-γ)) for discounted MDPs with γ > 1/2, where R(1-γ) > 0. |
1 Citations | The paper discusses the relationship between convergence rates and learning rates in Q-learning, particularly for polynomial learning rates, in complex conditions. |
| The learning rate for complex condition Q-learning should be constant to achieve a sample complexity of 1/μmin(1 - γ)5e2 + tmix/μmin(1 - γ) with variance reduction. | |
| Learning rates in Q-learning vary based on complexity. Polynomial rates (1/tω) with ω∈(1/2,1) lead to polynomial convergence, while linear rates (1/t) result in exponential convergence. |
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