Abstract: The disclosure provides a method and an apparatus for renewable-energy allocation based on reinforcement learning for an energy aggregator having an energy storage system (ESS) to coordinate renewable-energy allocation between energy suppliers and energy demanders. In the method, historical power-generation data of each supplier is collected and adapted to generate a renewable-energy index representing uncertainty of renewable energy. Multiple market indexes related to renewable-energy market dynamics are collected and integrated with the renewable-energy index and electricity information of the ESS to generate multiple states of a Markov decision process. The states are inputted to a reinforcement learning model to determine a price for the suppliers and the demanders. According to a supply amount and a demand amount proposed by the suppliers and the demanders in response to the price, the ESS coordinates the supply and demand of the renewable energy, and the reinforcement learning model is updated.

Abstract: The disclosure provides a method and an apparatus for renewable-energy allocation based on reinforcement learning for an energy aggregator having an energy storage system (ESS) to coordinate renewable-energy allocation between energy suppliers and energy demanders. In the method, historical power-generation data of each supplier is collected and adapted to generate a renewable-energy index representing uncertainty of renewable energy. Multiple market indexes related to renewable-energy market dynamics are collected and integrated with the renewable-energy index and electricity information of the ESS to generate multiple states of a Markov decision process. The states are inputted to a reinforcement learning model to determine a price for the suppliers and the demanders. According to a supply amount and a demand amount proposed by the suppliers and the demanders in response to the price, the ESS coordinates the supply and demand of the renewable energy, and the reinforcement learning model is updated.

Abstract: The disclosure provides a communication time allocation method using reinforcement learning for a wireless powered communication network and a base station. The method includes: determining a communication time allocation corresponding to the t-th time block according to an objective function associated with the total estimated throughput of the communication nodes; requesting each communication node to perform specific communication behaviors according to the corresponding communication time interval in the t-th time block; obtaining the actual throughput of each communication node in the t-th time block; generating the weight vector of each communication node in the (t+1)-th time block according to the actual throughput, the weight vector, and the estimated throughput of each communication node in the t-th time block.

Abstract: An apparatus and a method for peer-to-peer energy sharing based on reinforcement learning are provided.

Abstract: A method and an apparatus for baseball strategy planning based on reinforcement learning are provided. The method includes steps below. Historical data of innings in past games of a team is collected. Multiple game states, multiple offensive and defensive actions, and multiple rewards corresponding to multiple offensive and defensive results are defined based on multiple offensive and defensive processes occurring during the game, and are used to establish a Q table. The Q table is updated according to multiple combinations of the game state, the offensive and defensive action, and the offensive and defensive result recorded in the historical data. According to a current game state, Q values of all offensive and defensive actions executable in the current game state recorded in the updated Q table are sorted, and the offensive and defensive action suitable for being executed in the current game state is recommended according to a sorting result.

Abstract: The disclosure provides a communication time allocation method using reinforcement learning for a wireless powered communication network and a base station. The method includes: determining a communication time allocation corresponding to the t-th time block according to an objective function associated with the total estimated throughput of the communication nodes; requesting each communication node to perform specific communication behaviors according to the corresponding communication time interval in the t-th time block; obtaining the actual throughput of each communication node in the t-th time block; generating the weight vector of each communication node in the (t+1)-th time block according to the actual throughput, the weight vector, and the estimated throughput of each communication node in the t-th time block.

Abstract: A method and an apparatus for reinforcement learning based energy bidding, adapted for an energy aggregator to determine the energy supply configuration between multiple energy suppliers and multiple energy demanders, are provided. In the method, a supply amount of each energy supplier and a demand amount of each energy demander are acquired. A total demand amount of the energy demanders is calculated and replied to each energy supplier, and a total supply amount of the energy suppliers is calculated and replied to each energy demander. An electricity purchase quotation determined by each energy demander according to respective demand amount and the total supply amount, and an electricity sale quotation determined by each energy supplier according to respective supply amount and the total demand amount are received. A linear programming method is adopted to determine the energy supply configuration between the energy suppliers and the energy demanders according to information.

Abstract: A method and an apparatus for reinforcement learning based energy bidding, adapted for an energy aggregator to determine a power supply configuration between multiple energy suppliers and multiple energy demanders, are provided. In the method, a supply amount of each energy supplier and a demand amount of each energy demander are acquired. A total demand amount of the energy demanders is calculated and replied to each energy supplier, and a total supply amount of the energy suppliers is calculated and replied to each energy demander. An electricity purchase quotation determined by each energy demander according to respective demand amount and the total supply amount, and an electricity sale quotation determined by each energy supplier according to respective supply amount and the total demand amount are received. A linear programming method is adopted to determine the energy supply configuration between the energy suppliers and the energy demanders according to aforesaid information.

Abstract: A waveguide structure is formed in the present invention. With the structure, a yield of a cleaving process is improved. A high responsivity and a low sensitivity can be achieved. And an error tolerance for a production is also increased. The present invention can be applied to optoelectronic elements, such as an optical diode and a light modulator.