Chaoxu Mu

Wenzhang Liu, Wenzhe Cai, Kun Jiang et al.

In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that supports CPU, GPU, and Ascend, and can be executed on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS. Extensive benchmarks conducted on popular environments including MuJoCo, Atari, and StarCraftII multi-agent challenge demonstrate the library's impressive performance. XuanCe is open-source and can be accessed at https://github.com/agi-brain/xuance.git.

Hui Wang, Fafa Zhang, Chaoxu Mu

Multi-Criteria Decision Making~(MCDM) is widely applied in various fields, using quantitative and qualitative analyses of multiple levels and attributes to support decision makers in making scientific and rational decisions in complex scenarios. However, traditional MCDM methods face bottlenecks in high-dimensional problems. Given the fact that Large Language Models~(LLMs) achieve impressive performance in various complex tasks, but limited work evaluates LLMs in specific MCDM problems with the help of human domain experts, we further explore the capability of LLMs by proposing an LLM-based evaluation framework to automatically deal with general complex MCDM problems. Within the framework, we assess the performance of various typical open-source models, as well as commercial models such as Claude and ChatGPT, on 3 important applications, these models can only achieve around 60\% accuracy rate compared to the evaluation ground truth. Upon incorporation of Chain-of-Thought or few-shot prompting, the accuracy rates rise to around 70\%, and highly depend on the model. In order to further improve the performance, a LoRA-based fine-tuning technique is employed. The experimental results show that the accuracy rates for different applications improve significantly to around 95\%, and the performance difference is trivial between different models, indicating that LoRA-based fine-tuned LLMs exhibit significant and stable advantages in addressing MCDM tasks and can provide human-expert-level solutions to a wide range of MCDM challenges.

Wenzhang Liu, Lianjun Jin, Lu Ren et al.

Intelligent decision-making within large and redundant action spaces remains challenging in deep reinforcement learning. Considering similar but ineffective actions at each step can lead to repetitive and unproductive trials. Existing methods attempt to improve agent exploration by reducing or penalizing redundant actions, yet they fail to provide quantitative and reliable evidence to determine redundancy. In this paper, we propose a method to improve exploration efficiency by estimating the causal effects of actions. Unlike prior methods, our approach offers quantitative results regarding the causality of actions for one-step transitions. We first pre-train an inverse dynamics model to serve as prior knowledge of the environment. Subsequently, we classify actions across the entire action space at each time step and estimate the causal effect of each action to suppress redundant actions during exploration. We provide a theoretical analysis to demonstrate the effectiveness of our method and present empirical results from simulations in environments with redundant actions to evaluate its performance. Our implementation is available at https://github.com/agi-brain/cee.git.

Hui Wang, Xufeng Zhang, Chaoxu Mu

Heuristics have achieved great success in solving combinatorial optimization problems~(COPs). However, heuristics designed by humans require too much domain knowledge and testing time. Since Large Language Models~(LLMs) possess strong capabilities to understand and generate content with a knowledge base that covers various domains, they offer potential ways to automatically optimize heuristics. To this end, we propose Planning of Heuristics~(PoH), an optimization method that integrates LLM self-reflection with Monte Carlo Tree Search, a well-known planning algorithm. PoH iteratively refines generated heuristics by evaluating their performance and providing improvement suggestions. Our method enables to iteratively evaluate the generated heuristics~(states) and improve them based on the improvement suggestions~(actions) and evaluation results~(rewards), by effectively simulating future states to search for paths with higher rewards. In this paper, we apply PoH to solve the Traveling Salesman Problem and the Flow Shop Scheduling Problem. The experimental results show that PoH outperforms hand-crafted heuristics and other Automatic Heuristic Design methods based on LLMs, and achieves the state-of-the-art performance in automating heuristic optimization with LLMs to solve tested COPs, especially with large sizes.

Hui Wang, Xufeng Zhang, Xiaoyu Zhang et al.

Recently, Gumbel AlphaZero~(GAZ) was proposed to solve classic combinatorial optimization problems such as TSP and JSSP by creating a carefully designed competition model~(consisting of a learning player and a competitor player), which leverages the idea of self-play. However, if the competitor is too strong or too weak, the effectiveness of self-play training can be reduced, particularly in complex CO problems. To address this problem, we further propose a two-stage self-play strategy to improve the GAZ method~(named TSS GAZ PTP). In the first stage, the learning player uses the enhanced policy network based on the Gumbel Monte Carlo Tree Search~(MCTS), and the competitor uses the historical best trained policy network~(acts as a greedy player). In the second stage, we employ Gumbel MCTS for both players, which makes the competition fiercer so that both players can continuously learn smarter trajectories. We first investigate the performance of our proposed TSS GAZ PTP method on TSP since it is also used as a test problem by the original GAZ. The results show the superior performance of TSS GAZ PTP. Then we extend TSS GAZ PTP to deal with multi-constrained Electric Vehicle Routing Problems~(EVRP), which is a recently well-known real application research topic and remains challenging as a complex CO problem. Impressively, the experimental results show that the TSS GAZ PTP outperforms the state-of-the-art Deep Reinforcement Learning methods in all types of instances tested and outperforms the optimization solver in tested large-scale instances, indicating the importance and promising of employing more dynamic self-play strategies for complex CO problems.