Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithm...Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithms for average reward problems, a novel incremental algorithm, called R( λ ) learning, was proposed. Results and Conclusion The proposed algorithm is a natural extension of the Q( λ) learning, the multi step discounted reward reinforcement learning algorithm, to the average reward cases. Simulation results show that the R( λ ) learning with intermediate λ values makes significant performance improvement over the simple R learning.展开更多
Leptin is the protein product encoded by the obese (ob)gene. It is a circulating hormone produced primarily by the adipose tissue. ob/ob mice with mutations of the gene encoding leptin become morbidly obese, infertile...Leptin is the protein product encoded by the obese (ob)gene. It is a circulating hormone produced primarily by the adipose tissue. ob/ob mice with mutations of the gene encoding leptin become morbidly obese, infertile, hyperphagic, hypothermic,and diabetic. Since the cloning of leptin in 1994, our knowledge in body weight regulation and the role played by leptin has increased substantially. We now know that leptin signals through its receptor, OB-R, which is a member of the cytokine receptor superfamily. Leptin serves as an adiposity signal to inform the brain the adipose tissue mass in a negative feedback loop regulating food intake and energy expenditure. Leptin also plays important roles in angiogenesis, immune function, fertility and bone formation. Humans with mutations in the gene encoding leptin are also morbidly obese and respond to leptin treatment,demonstrating that enhancing or inhibiting leptin’s activities in vivo may have potential therapeutic benefits.展开更多
文摘Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithms for average reward problems, a novel incremental algorithm, called R( λ ) learning, was proposed. Results and Conclusion The proposed algorithm is a natural extension of the Q( λ) learning, the multi step discounted reward reinforcement learning algorithm, to the average reward cases. Simulation results show that the R( λ ) learning with intermediate λ values makes significant performance improvement over the simple R learning.
文摘Leptin is the protein product encoded by the obese (ob)gene. It is a circulating hormone produced primarily by the adipose tissue. ob/ob mice with mutations of the gene encoding leptin become morbidly obese, infertile, hyperphagic, hypothermic,and diabetic. Since the cloning of leptin in 1994, our knowledge in body weight regulation and the role played by leptin has increased substantially. We now know that leptin signals through its receptor, OB-R, which is a member of the cytokine receptor superfamily. Leptin serves as an adiposity signal to inform the brain the adipose tissue mass in a negative feedback loop regulating food intake and energy expenditure. Leptin also plays important roles in angiogenesis, immune function, fertility and bone formation. Humans with mutations in the gene encoding leptin are also morbidly obese and respond to leptin treatment,demonstrating that enhancing or inhibiting leptin’s activities in vivo may have potential therapeutic benefits.
