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Found 3 papers, 1 papers with code
Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning
Deep Reinforcement Learning (RL) is well known for being highly sensitive to hyperparameters, requiring practitioners substantial efforts to optimize them for the problem at hand.
Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
It has been observed that this scheme can be potentially generalized to carry out multiple iterations of the Bellman operator at once, benefiting the underlying learning algorithm.
Parameterized Projected Bellman Operator
We formulate an optimization problem to learn PBO for generic sequential decision-making problems, and we theoretically analyze its properties in two representative classes of RL problems.
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