Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial nature of such games allows for conceptually simple and principled application of RL methods. However real-world settings are many-agent, and agent interactions are complex mixtures of common-interest and competitive aspects. We consider Diplomacy, a 7-player board game designed to accentuate dilemmas resulting from many-agent interactions. It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms. We propose a simple yet effective approximate best response operator, designed to handle large combinatorial action spaces and simultaneous moves. We also introduce a family of policy iteration methods that approximate fictitious play. With these methods, we successfully apply RL to Diplomacy: we show that our agents convincingly outperform the previous state-of-the-art, and game theoretic equilibrium analysis shows that the new process yields consistent improvements.

The repeated reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test error rates. Verifying whether a learned model is overfitted to a test set is challenging as independent test sets drawn from the same data distribution are usually unavailable, while other test sets may introduce a distribution shift. We propose a new hypothesis test that uses only the original test data to detect overfitting. It utilizes a new unbiased error estimate that is based on adversarial examples generated from the test data and importance weighting. Overfitting is detected if this error estimate is sufficiently different from the original test error rate. The power of the method is illustrated using Monte Carlo simulations on a synthetic problem. We develop a specialized variant of our dependence detector for multiclass image classification, and apply it to testing overfitting of recent models to two popular real-world image classification benchmarks. In the case of ImageNet, our method was not able to detect overfitting to the test set for a state-of-the-art classifier, while on CIFAR-10 we found strong evidence of overfitting for the two recent model architectures we considered, and weak evidence of overfitting on the level of individual training runs.