Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Off-policy learning refers to the problem of learning the value function of a way of behaving, or policy, while following a different policy. Gradient-based off-policy learning algorithms, such as GTD and TDC/GQ, converge even when using function approximation and incremental updates. Hover, they have been developed for the case of a fixed behavior policy. In control problems, one would like to adapt the behavior policy over time to become more greedy with respect to the existing value function. ,  present the first gradient-based learning algorithms for this problem, which rely on the framework of policy gradient in order to modify the behavior policy.  present derivations of the algorithms, a convergence theorem, and empirical evidence showing that they compare favorably to existing approaches.
['Lucas Lehnert', 'Doina Precup']
I am writing a research paper on a idea Policy Gradient Methods for Off-policy Control
[{"from": "human", "value": "I am writing a research paper on a idea Policy Gradient Methods for Off-policy Control"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Off-policy learning refers to the problem of learning the value function of a way of behaving, or policy, while following a different policy. Gradient-based off-policy learning algorithms, such as GTD and TDC/GQ, converge even when using function approximation and incremental updates. Hover, they have been developed for the case of a fixed behavior policy. In control problems, one would like to adapt the behavior policy over time to become more greedy with respect to the existing value function. ,  present the first gradient-based learning algorithms for this problem, which rely on the framework of policy gradient in order to modify the behavior policy.  present derivations of the algorithms, a convergence theorem, and empirical evidence showing that they compare favorably to existing approaches."}]