Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For undiscounted reinforcement learning in Markov decision processes (MDPs)  consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP  propose a new parameter: An MDP has diameter D if for any pair of states s, s' there is a policy which moves from s to s' in at most D steps (on average).  present a reinforcement learning algorithm with total regret O(DS √AT) after T steps for any unknown MDP with S states, A actions per state, and diameter D. This bound holds with high probability.  also present a corresponding lor bound of Ω(√DSAT) on the total regret of any learning algorithm.
['Peter Auer', 'Thomas Jaksch', 'Ronald Ortner']
I am writing a research paper on a idea Near-optimal Regret Bounds for Reinforcement Learning
[{"from": "human", "value": "I am writing a research paper on a idea Near-optimal Regret Bounds for Reinforcement Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For undiscounted reinforcement learning in Markov decision processes (MDPs)  consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP  propose a new parameter: An MDP has diameter D if for any pair of states s, s' there is a policy which moves from s to s' in at most D steps (on average).  present a reinforcement learning algorithm with total regret O(DS \u221aAT) after T steps for any unknown MDP with S states, A actions per state, and diameter D. This bound holds with high probability.  also present a corresponding lor bound of \u03a9(\u221aDSAT) on the total regret of any learning algorithm."}]