Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the problem of autonomously learning controllers for visioncapable mobile robots.  extend McCallum’s (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories.  demonstrate the feasibility of our approach by successfully running our algorithm on a real mobile robot. The algorithm is novel and unique in that it (a) explores the environment and learns directly on a mobile robot without using a hand-made computer model as an intermediate step, (b) does not require manual discretization of the sensor input space, (c) works in piecewise continuous perceptual spaces, and (d) copes with partial observability. Together this allows learning from much less experience compared to previous methods.
['Viktor Zhumatiy', 'Faustino J. Gomez', 'Marcus Hutter', 'Jürgen Schmidhuber']
I am writing a research paper on a idea Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot
[{"from": "human", "value": "I am writing a research paper on a idea Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the problem of autonomously learning controllers for visioncapable mobile robots.  extend McCallum\u2019s (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories.  demonstrate the feasibility of our approach by successfully running our algorithm on a real mobile robot. The algorithm is novel and unique in that it (a) explores the environment and learns directly on a mobile robot without using a hand-made computer model as an intermediate step, (b) does not require manual discretization of the sensor input space, (c) works in piecewise continuous perceptual spaces, and (d) copes with partial observability. Together this allows learning from much less experience compared to previous methods."}]