Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many control problems take place in continuous state-action spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control policies from scratch, its applicability to higher dimensional continuous state-action spaces remains rather limited to date. Instead of learning from scratch, in this paper  suggest to learn a desired complex control policy by transforming an existing simple canonical control policy. For this purpose,  represent canonical policies in terms of differential equations with ll-defined attractor properties. By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability properties of the canonical system.  demonstrate our techniques in the context of learning a set of movement skills for a humanoid robot from demonstrations of a human teacher. Policies are acquired rapidly, and, due to the properties of ll formulated differential equations, can be re-used and modified on-line under dynamic changes of the environment. The linear parameterization of nonparametric regression moreover lends itself to recognize and classify previously learned movement skills. Evaluations in simulations and on an actual 30 degree-of-freedom humanoid robot exemplify the feasibility and robustness of our approach.
['Auke Jan Ijspeert', 'Jun Nakanishi', 'Stefan Schaal']
I am writing a research paper on a idea Learning Attractor Landscapes for Learning Motor Primitives
[{"from": "human", "value": "I am writing a research paper on a idea Learning Attractor Landscapes for Learning Motor Primitives"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many control problems take place in continuous state-action spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control policies from scratch, its applicability to higher dimensional continuous state-action spaces remains rather limited to date. Instead of learning from scratch, in this paper  suggest to learn a desired complex control policy by transforming an existing simple canonical control policy. For this purpose,  represent canonical policies in terms of differential equations with ll-defined attractor properties. By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability properties of the canonical system.  demonstrate our techniques in the context of learning a set of movement skills for a humanoid robot from demonstrations of a human teacher. Policies are acquired rapidly, and, due to the properties of ll formulated differential equations, can be re-used and modified on-line under dynamic changes of the environment. The linear parameterization of nonparametric regression moreover lends itself to recognize and classify previously learned movement skills. Evaluations in simulations and on an actual 30 degree-of-freedom humanoid robot exemplify the feasibility and robustness of our approach."}]