Papers

Hamiltonian systems represent an important class of dynamical systems such as pendulums, molecular dynamics, and cosmic systems. The choice of solvers is significant to the accuracy when simulating Hamiltonian systems, where symplectic solvers show great significance. Recent advances in neural network-based hypersolvers, though achieve competitive results, still lack the symplecity necessary for reliable simulations, especially over long time horizons. To alleviate this, we introduce Hyperverlet, a new hypersolver composing the traditional, symplectic velocity Verlet and symplectic neural network-based solvers. More specifically, we propose a parameterization of symplectic neural networks and prove that hyperbolic tangent is r-finite expanding the set of allowable activation functions for symplectic neural networks, improving the accuracy. Extensive experiments on a spring-mass and a pendulum system justify the design choices and suggest that Hyperverlet outperforms both traditional solvers and hypersolvers.

poster session 1 @ red 2, poster session 10 @ red 2, oral session 1 @ red 2, poster session 1, poster session 10, oral session 1

Heads-up no-limit Texas hold'em (HUNL) is the quintessential game with imperfect information. Representative prior works like DeepStack and Libratus heavily rely on counterfactual regret minimization (CFR) and its variants to tackle HUNL. However, the prohibitive computation cost of CFR iteration makes it difficult for subsequent researchers to learn the CFR model in HUNL and apply it in other practical applications. In this work, we present AlphaHoldem, a high-performance and lightweight HUNL AI obtained with an end-to-end self-play reinforcement learning framework. The proposed framework adopts a pseudo-Siamese architecture to directly learn from the input state information to the output actions by competing the learned model with its different historical versions. The main technical contributions include a novel state representation of card and betting information, a multi-task self-play training loss function, and a new model evaluation and selection metric to generate the final model. In a study involving 100,000 hands of poker, AlphaHoldem defeats Slumbot and DeepStack using only one PC with three days training. At the same time, AlphaHoldem only takes four milliseconds for each decision-making using only a single CPU core, more than 1,000 times faster than DeepStack. We will provide an online testing platform of AlphaHoldem to facilitate further studies in this direction.

poster session 1 @ red 2, poster session 10 @ red 2, oral session 1 @ red 2, poster session 1, poster session 10, oral session 1

Recently, deep reinforcement learning (RL) has proven its feasibility in solving combinatorial optimization problems (COPs). The learning-to-rank techniques have been studied in the field of information retrieval. While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs. In this paper, we present the learning-to-rank distillation-based COP framework, where a high-performance ranking policy obtained by RL for a COP can be distilled into a non-iterative, simple model, thereby achieving a low-latency COP solver. Specifically, we employ the approximated ranking distillation to render a score-based ranking model learnable via gradient descent. Furthermore, we use the efficient sequence sampling to improve the inference performance with a limited delay. With the framework, we demonstrate that a distilled model not only achieves comparable performance to its respective, high-performance RL, but also provides several times faster inferences. We evaluate the framework with several COPs such as priority-based task scheduling and multidimensional knapsack, demonstrating the benefits of the framework in terms of inference latency and performance.

poster session 1 @ red 2, poster session 10 @ red 2, oral session 1 @ red 2, poster session 1, poster session 10, oral session 1