Online Meta-learning by Parallel Algorithm Competition

The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulates the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question in reinforcement learning, which arguably has become more of an issue recently with the success of deep reinforcement learning in high-dimensional state spaces. The long learning times in domains such as Atari 2600 video games makes it not feasible to perform comprehensive searches of appropriate meta-parameter values. We propose the Online Meta-learning by Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several instances of a reinforcement learning algorithm are run in parallel with small differences in the initial values of the meta-parameters. After a fixed number of episodes, the instances are selected based on their performance in the task at hand. Before continuing the learning, Gaussian noise is added to the meta-parameters with a predefined probability. We validate the OMPAC method by improving the state-of-the-art results in stochastic SZ-Tetris and in standard Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600 games by 62% or more. The experiments also show the ability of the OMPAC method to adapt the meta-parameters according to the learning progress in different tasks.Comment: 15 pages, 10 figures. arXiv admin note: text overlap with arXiv:1702.0311

Phase II study of S-1, a novel oral fluorouracil, in advanced non-small-cell lung cancer

The purpose of this study was to evaluate the efficacy and safety of a novel oral anticancer fluoropyrimidine derivative, S-1, in patients receiving initial chemotherapy for unresectable, advanced non-small-cell lung cancer (NSCLC). Between June 1996 and July 1998, 62 patients with NSCLC who had not received previous chemotherapy for advanced disease were enrolled in this study. 59 patients (22 stage IIIB and 37 stage IV) were eligible for the evaluation of efficacy and safety. S-1 was administered orally, twice daily, after meals. 3 dosages of S-1 were prescribed according to body surface area (BSA) so that they would be approximately equivalent to 80 mg m−2day−1: BSA < 1.25 m2, 40 mg b.i.d.; BSA≥1.25 but <1.5 m2; 50 mg b.i.d., and BSA≥1.5 m2: 60 mg b.i.d. One cycle consisted of consecutive administration of S-1 for 28 days followed by a 2-week rest period, and cycles were repeated up to 4 times. The partial response (PR) rate of the eligible patients was 22.0% (13/59); (95% confidence interval: 12.3–34.7%). A PR was observed in 22.7% (5/22) of the stage IIIB patients and 21.6% (8/37) of the stage IV patients. The median response duration was 3.4 months (1.1–13.7 months or longer). Grade 4 neutropenia was observed in one of the 59 patients (1.7%). The grade 3 or 4 toxicities consisted of decreased haemoglobin level in 1.7% of patients (1/59), neutropenia in 6.8% (4/59), thrombocytopenia in 1.7% (1/59), anorexia in 10.2% (6/59), diarrhoea in 8.5% (5/59), stomatitis in 1.7% (1/59), and malaise in 6.8% (4/59), and their incidences were relatively low. There were no irreversible, severe or unexpected toxicities. The median survival time (MST) of all patients was 10.2 months (95% confidence interval: 7.7–14.5 months), and the one-year survival rate was 41.1%. The MST of the stage IIIB patients was 7.9 months, and that of the stage IV patients was 11.1 months. The one-year survival rates of the stage IIIB and IV patients were 30.7% and 47.4%, respectively. S-1 was considered to be an active single agent against NSCLC. Further study of S-1 with other active agents is warranted. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Replicator dynamics in public goods games with reward funds

Which punishment or rewards are most effective at maintaining cooperation in public goods interaction and deterring defectors who are willing to freeload on others' contribution? The sanction system is itself a public good and can cause problematic "second-order free riders" who do not contribute to the provisions of the sanctions and thus may subvert the cooperation supported by sanctioning. Recent studies have shown that public goods games with punishment can lead to a coercion-based regime if participation in the game is optional. Here, we reveal that even with compulsory participation, rewards can maintain cooperation within an infinitely large population. We consider three strategies for players in a standard public goods game to be a cooperator or a defector in a standard public goods game, or to be a rewarder who contributes to the public good and to a fund that rewards players who contribute during the game. Cooperators do not contribute to the reward fund and are therefore classified as second-order free riders. The replicator dynamics for the three strategies exhibit a rock-scissors-paper cycle, and can be analyzed fully, despite the fact that the expected payoffs are nonlinear. The model does not require repeated interaction, spatial structure, group selection, or reputation. We also discuss a simple method for second-order sanctions, which can lead to a globally stable state where 100% of the population are rewarders