Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on the action and context.  consider this problem under a realizability assumption: there exists a function in a (known) function class, always capable of predicting the expected reward, given the action and context. Under this assumption,  show three things.  present a new algorithm—Regressor Elimination— with a regret similar to the agnostic setting (i.e. in the absence of realizability assumption).  prove a new lor bound showing no algorithm can achieve superior performance in the worst case even with the realizability assumption. Hover,  do show that for any set of policies (mapping contexts to actions), there is a distribution over rewards (given context) such that our new algorithm has constant regret unlike the previous approaches.
['Alekh Agarwal', 'Miroslav Dudík', 'Satyen Kale', 'John Langford', 'Robert E. Schapire']
I am writing a research paper on a idea Contextual bandit learning with predictable rewards
[{"from": "human", "value": "I am writing a research paper on a idea Contextual bandit learning with predictable rewards"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on the action and context.  consider this problem under a realizability assumption: there exists a function in a (known) function class, always capable of predicting the expected reward, given the action and context. Under this assumption,  show three things.  present a new algorithm\u2014Regressor Elimination\u2014 with a regret similar to the agnostic setting (i.e. in the absence of realizability assumption).  prove a new lor bound showing no algorithm can achieve superior performance in the worst case even with the realizability assumption. Hover,  do show that for any set of policies (mapping contexts to actions), there is a distribution over rewards (given context) such that our new algorithm has constant regret unlike the previous approaches."}]