Industrial automation in smart logistics and factories requires simulation platforms that support rapid environment building before costly physical deployment. Yet existing tools often require substantial expertise, complex setup, and long configuration times, hindering agile prototyping. We present RAPID, a simulation platform with two components: layout design, which enables intuitive visual configuration of factory layouts, and behavior simulation and validation, which allows users to attach behavior models and evaluate system performance. RAPID lowers the entry barrier to industrial simulation, letting users apply existing behavior models or trained reinforcement learning (RL) agents to new layouts with minimal effort. This approach lets practitioners prototype facilities in minutes rather than weeks and gives researchers a standardized environment for benchmarking multi-agent RL and coordination algorithms. By combining rapid design with simulation-based validation, RAPID accelerates automation development from concept to implementation.

Reinforcement learning (RL) has evolved beyond monolithic training, yet existing frameworks remain limited to single algorithms or simple offline-to-online transitions. We present multi-phase RL, a framework that orchestrates multiple learning phases for continual policy improvement. It enables efficient fine-tuning of pretrained policies with new data and smooth adaptation from simulation to real-world environments. To support this paradigm, we introduce RL-Studio, a platform that addresses key implementation barriers, including neural architecture mismatches, parameter transfer complexities, and experiment management overhead. It provides phase orchestration, transition-point monitoring, and full experiment lineage tracking. We demonstrate the effectiveness of multi-phase RL through representative scenarios and highlight RL-Studio’s capabilities.

Trajectory prediction is a crucial task in modeling human behavior, especially in safety-critical fields such as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy, while recently proposed deep learning approaches suffer from computational cost, slow inference speed, lack of explainability, and generalization issues that limit their practical adoption in such environments. In this paper, we introduce TrajEvo, a framework that leverages Large Language Models (LLMs) to automatically design trajectory prediction heuristics. TrajEvo employs an evolutionary algorithm to generate and refine prediction heuristics from past trajectory data. We introduce a Cross-Generation Elite Sampling to promote population diversity and a Statistics Feedback Loop allowing the LLM to analyze alternative predictions. Our evaluations show TrajEvo outperforms previous heuristic methods on various real-world datasets, and remarkably outperforms both heuristics and deep learning methods when generalizing to an unseen real-world dataset. TrajEvo represents a first step toward automated design of fast, explainable, and generalizable trajectory prediction heuristics. We make our source code publicly available to foster future research.

The rapid increase in high-rise building construction has intensified the need for efficient elevator system operations. This article addresses the elevator dispatching problem in elevator group control systems. We formulate the problem as a Semi-Markov Decision Process (SMDP), defining the state representation, action space, and reward function. A two-phase model is then introduced, integrating imitation learning and deep reinforcement learning techniques to derive the optimal elevator dispatching policy from the formulated SMDP. In the first phase, a policy network is pre-trained by estimating the time required for elevator cars to pick up assigned hall requests. In the second phase, the pre-trained policy network is further optimized using Proximal Policy Optimization (PPO), a well-known policy-based deep reinforcement learning method. Additionally, we propose a novel update interval, termed the “direct-effect” interval, which improves policy training during the reinforcement learning phase. Notably, this direct-effect interval concept has potential applicability to other multi-resource scheduling problems. Empirical experiments demonstrate the advantages of incorporating imitation learning before reinforcement learning, as well as the effectiveness of employing the direct-effect update interval during the reinforcement learning phase. Furthermore, the proposed model outperforms various benchmark rules in terms of average waiting time and the distribution of long waiting times, as validated across four traffic patterns.

The offline datasets for imitation learning (IL) in multi-agent games typically contain player trajectories exhibiting diverse strategies, which necessitate measures to prevent learning algorithms from acquiring undesirable behaviors. Learning representations for these trajectories is an effective approach to depicting the strategies employed by each demonstrator. However, existing learning strategies often require player identification or rely on strong assumptions, which are not appropriate for multi-agent games. Therefore, in this paper, we introduce the Strategy Representation for Imitation Learning (STRIL) framework, which (1) effectively learns strategy representations in multi-agent games, (2) estimates proposed indicators based on these representations, and (3) filters out sub-optimal data using the indicators. STRIL is a plug-in method that can be integrated into existing IL algorithms. We demonstrate the effectiveness of STRIL across competitive multi-agent scenarios, including Two-player Pong, Limit Texas Hold'em, and Connect Four. Our approach successfully acquires strategy representations and indicators, thereby identifying dominant trajectories and significantly enhancing existing IL performance across these environments.

Multi-Agent Reinforcement Learning (MARL) struggles with coordination in sparse reward environments. Macro-actions —sequences of actions executed as single decisions— facilitate long-term planning but introduce asynchrony, complicating Centralized Training with Decentralized Execution (CTDE). Existing CTDE methods use padding to handle asynchrony, risking misaligned asynchronous experiences and spurious correlations. We propose the Agent-Centric Actor-Critic (ACAC) algorithm to manage asynchrony without padding. ACAC uses agent-centric encoders for independent trajectory processing, with an attention-based aggregation module integrating these histories into a centralized critic for improved temporal abstractions. The proposed structure is trained via a PPO-based algorithm with a modified Generalized Advantage Estimation for asynchronous environments. Experiments show ACAC accelerates convergence and enhances performance over baselines in complex MARL tasks.

Reinforcement learning with offline data suffers from Q-value extrapolation errors. To address this issue, we first demonstrate that linear extrapolation of the Q-function beyond the data range is particularly problematic. To mitigate this, we propose guiding the gradual decrease of Q-values outside the data range, which is achieved through reward scaling with layer normalization (RS-LN) and a penalization mechanism for infeasible actions (PA). By combining RS-LN and PA, we develop a new algorithm called PARS. We evaluate PARS across a range of tasks, demonstrating superior performance compared to state-of-the-art algorithms in both offline training and online fine-tuning on the D4RL benchmark, with notable success in the challenging AntMaze Ultra task.

Offline-to-online reinforcement learning (RL) aims to integrate the complementary strengths of offline and online RL by pre-training an agent offline and subsequently fine-tuning it through online interactions. However, recent studies reveal that offline pre-trained agents often underperform during online fine-tuning due to inaccurate value estimation caused by distribution shift, with random initialization proving more effective in certain cases. In this work, we propose a novel method, Online Pre-Training for Offline-to-Online RL (OPT), explicitly designed to address the issue of inaccurate value estimation in offline pre-trained agents. OPT introduces a new learning phase, Online Pre-Training, which allows the training of a new value function tailored specifically for effective online fine-tuning. Implementation of OPT on TD3 and SPOT demonstrates an average 30% improvement in performance across a wide range of D4RL environments, including MuJoCo, Antmaze, and Adroit.

Multi-Agent Reinforcement Learning (MARL) has shown promise in solving complex problems involving cooperation and competition among agents, such as an Unmanned Surface Vehicle (USV) swarm used in search and rescue, surveillance, and vessel protection. However, aligning system behavior with user preferences is challenging due to the difficulty of encoding expert intuition into reward functions. To address the issue, we propose a Reinforcement Learning with Human Feedback (RLHF) approach for MARL that resolves credit-assignment challenges through an Agent-Level Feedback system categorizing feedback into intra-agent, inter-agent, and intra-team types. To overcome the challenges of direct human feedback, we employ a Large Language Model (LLM) evaluator to validate our approach using feedback scenarios such as region constraints, collision avoidance, and task allocation. Our method effectively refines USV swarm policies, addressing key challenges in multi-agent systems while maintaining fairness and performance consistency.

Reinforcement learning (RL) has emerged as an effective approach for signal control, and several studies attempt to apply RL to network-wide signal control problems. Most existing methods for network-wide signal controls simply apply the pretrained model independently to the network or simply expand the observation range for the neighbors. While several recent studies have proposed cooperative signal control models using multi-agent reinforcement learning (MARL) approaches, they have not considered the transferability of the policy. This issue becomes more critical in treating a multi-agent signal control problem, since there exist innumerable possibilities of data distributions for the demand patterns. Hence, this research aims to develop a transferable and cooperative multi-agent traffic signal control model. As the key idea, we propose a decentralized and communication-based MARL approach that considers the spatial correlation of traffic dynamics in the model design and employs a graph representation for the state. Three experiments in simulated environments of the Gangnam district in Seoul, South Korea are conducted to 1) validate the model, 2) assess the policy’s transferability, and 3) evaluate the efficiency of the multi-agent cooperation. The experimental results show that the proposed model obtains a transferable policy so that it adapts to the unexperienced demand scenario. In addition, it is concluded that the joint action of the proposed RL model cooperatively rebalances the traffic demands so that it improves the efficiency of the network-wide signal controls.

Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light management systems rely on human-crafted decisions, which may not adapt well to diverse traffic patterns. In this paper, we delve into two pivotal design components of the traffic light management system that can be dynamically adjusted to various traffic conditions: phase combination and phase time allocation. While numerous studies have sought an efficient strategy for managing traffic lights, most of these approaches consider a fixed traffic pattern and are limited to relatively small road networks. To overcome these limitations, we introduce a novel and practical framework to formulate the optimization of such design components using an offline meta black-box optimization. We then present a simple yet effective method to efficiently find a solution for the aforementioned problem. In our framework, we first collect an offline meta dataset consisting of pairs of design choices and corresponding congestion measures from various traffic patterns. After collecting the dataset, we employ the Attentive Neural Process (ANP) to predict the impact of the proposed design on congestion across various traffic patterns with well-calibrated uncertainty. Finally, Bayesian optimization, with ANP as a surrogate model, is utilized to find an optimal design for unseen traffic patterns through limited online simulations. Our experiment results show that our method outperforms state-of-the-art baselines on complex road networks in terms of the number of waiting vehicles. Surprisingly, the deployment of our method into a real-world traffic system was able to improve traffic throughput by 4.80% compared to the original strategy.

Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a black box with little explainability and often suffers from suboptimal performance, especially for autonomous navigation in highly interactive multi-agent environments. To address these issues, we propose three auxiliary tasks with spatio-temporal relational reasoning and integrate them into the standard DRL framework, which improves the decision making performance and provides explainable intermediate indicators. We propose to explicitly infer the internal states (i.e., traits and intentions) of surrounding agents (e.g., human drivers) as well as to predict their future trajectories in the situations with and without the ego agent through counterfactual reasoning. These auxiliary tasks provide additional supervision signals to infer the behavior patterns of other interactive agents. Multiple variants of framework integration strategies are compared. We also employ a spatio-temporal graph neural network to encode relations between dynamic entities, which enhances both internal state inference and decision making of the ego agent. Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents. To validate the proposed method, we design an intersection driving simulator based on the Intelligent Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics and provides explainable intermediate indicators (i.e., internal states, and interactivity scores) for decision making.

Defense modeling and simulation (DM&S) has brought insights into how to efficiently operate combat entities, such as soldiers and weapon systems. Most DM&S works have been developed to reflect accurate descriptions of military doctrines, yet these doctrines provide only guidelines of military operations, not details about how the combat entities should behave. Because such vague parts are often fulfilled with the appropriate behavior of combat entities in a battlefield, one part argues that DM&S should consider individual combat behaviors as well. However, it is known as an infeasible problem discovering best individual actions from infinite searching space, such as the battlefield. This paper proposes a layered behavior modeling to practically resolve this issue. The proposed method applies descriptive modeling to reduce the searching space by employing domain-specific knowledge; and prescriptive modeling to discover best individual actions in the reduced space. For the generalization, the proposed method adapts both modeling methods being modularized, and then the proposed method suggested an interface between them that is based on their semantic analogies. Both modeling methods are modularized, so they are interacted through an interface defined in the proposed method. This paper presents a realization of the proposed method through a case study of infantry company-level operations. In the case study, the proposed method is implemented with discrete event system specification formalism as the descriptive part and Markov decision process as the prescriptive part. The experimental results illustrated that the combat effectiveness resulted from the proposed method is statistically better than that from the descriptive-only modeling, and the difference would be guided by the objective of the combat behavior. Through the presented experimental results and the discussion, this paper argues that future DM&S should consider a broad spectrum from the battlefield incorporating the rational behavior of military individuals.

Bayesian reinforcement learning (BRL) provides a formal framework for optimal exploration-exploitation tradeoff in reinforcement learning. Unfortunately, it is generally intractable to find the Bayes-optimal behavior except for restricted cases. As a consequence, many BRL algorithms, model-based approaches in particular, rely on approximated models or real-time search methods. In this paper, we present potential-based shaping for improving the learning performance in model-based BRL. We propose a number of potential functions that are particularly well suited for BRL, and are domain-independent in the sense that they do not require any prior knowledge about the actual environment. By incorporating the potential function into real-time heuristic search, we show that we can significantly improve the learning performance in standard benchmark domains.

Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploitation tradeoff in reinforcement learning. We focus on model based BRL, which involves a compact formulation of the optimal tradeoff from the Bayesian perspective. However, it still remains a computational challenge to compute the Bayes-optimal policy. In this paper, we propose a novel approach to compute tighter value function bounds of the Bayes-optimal value function, which is crucial for improving the performance of many model-based BRL algorithms. We then present how our bounds can be integrated into real-time AO* heuristic search, and provide a theoretical analysis on the impact of improved bounds on the search efficiency. We also provide empirical results on standard BRL domains that demonstrate the effectiveness of our approach.