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AssigneeHitachi, Ltd.
InventorsHamed KHORASGANI, Chi ZHANG, Susumu SERITA, Chetan GUPTA
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Example implementations described herein involve a new reinforcement learning algorithm to address short-term goals. In the training step, the proposed solution learns the system dynamic model (short-term prediction) in a linear format in terms of actions. It also learns the expected rewards (long-term prediction) in a linear format in terms of actions. In the application step, the proposed solution uses the learned models plus simple optimization algorithms to find actions that satisfy both short-term goals and long-term goals. Through the example implementations, there is no need to design sensitive reward functions for achieving short-term and long-term goals concurrently. Further, there is better performance in achieving short-term and long-term goals compared to the traditional reward modification methods, and it is possible to modify the short-term goals without time-consuming retraining.
A method for determining actions through reinforcement learning with a short-term network and a long-term network, the method comprising:
A non-transitory computer readable medium, storing instructions for determining actions through reinforcement learning with a short-term network and a long-term network, the instructions comprising:
An apparatus configured for determining actions through reinforcement learning with a short-term network and a long-term network, the apparatus comprising:
Dependencies are inferred from the claim text.