Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Approximate value iteration (AVI) is a widely used technique in reinforcement learning. Most AVI methods do not take full advantage of the sequential relationship beten samples within a trajectory in deriving value estimates, due to the challenges in dealing with the inherent bias and variance in the n-step returns.  propose a bounding method which uses a negatively biased but relatively low variance estimator generated from a complex return to provide a lor bound on the observed value of a traditional one-step return estimator. In addition,  develop a new Bounded FQI algorithm, which efficiently incorporates the bounding method into an AVI framework. Experiments show that our method produces more accurate value estimates than existing approaches, resulting in improved policies.
['Robert Wright', 'Xingye Qiao', 'Lei Yu', 'Steven Loscalzo']
I am writing a research paper on a idea Improving approximate value iteration with complex returns by bounding
[{"from": "human", "value": "I am writing a research paper on a idea Improving approximate value iteration with complex returns by bounding"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Approximate value iteration (AVI) is a widely used technique in reinforcement learning. Most AVI methods do not take full advantage of the sequential relationship beten samples within a trajectory in deriving value estimates, due to the challenges in dealing with the inherent bias and variance in the n-step returns.  propose a bounding method which uses a negatively biased but relatively low variance estimator generated from a complex return to provide a lor bound on the observed value of a traditional one-step return estimator. In addition,  develop a new Bounded FQI algorithm, which efficiently incorporates the bounding method into an AVI framework. Experiments show that our method produces more accurate value estimates than existing approaches, resulting in improved policies."}]