Chayan Banerjee

Mining-Gym: A configurable RL benchmarking environment for open-pit truck dispatch optimization

Chayan Banerjee, Kien Nguyen, Clinton Fookes

CWM: Contrastive World Models for Action Feasibility Learning in Embodied Agent Pipelines

Chayan Banerjee

A reliable action feasibility scorer is a critical bottleneck in embodied agent pipelines: before any planning or reasoning occurs, the agent must identify which candidate actions are physically executable in the current state. Existing approaches use supervised fine-tuning (SFT) to train action …

Sustainable Multi-Agent Crowdsourcing via Physics-Informed Bandits

Chayan Banerjee

Crowdsourcing platforms face a four-way tension between allocation quality, workforce sustainability, operational feasibility, and strategic contractor behaviour--a dilemma we formalise as the Cold-Start, Burnout, Utilisation, and Strategic Agency Dilemma. Existing methods resolve at most two of these tensions simultaneously: greedy heuristics and …

Sustainable Multi-Agent Crowdsourcing via Physics-Informed Bandits

Chayan Banerjee

Crowdsourcing platforms face a four-way tension between allocation quality, workforce sustainability, operational feasibility, and strategic contractor behaviour--a dilemma we formalise as the Cold-Start, Burnout, Utilisation, and Strategic Agency Dilemma. Existing methods resolve at most two of these tensions simultaneously: greedy heuristics and …

CWM: Contrastive World Models for Action Feasibility Learning in Embodied Agent Pipelines

Chayan Banerjee

A reliable action feasibility scorer is a critical bottleneck in embodied agent pipelines: before any planning or reasoning occurs, the agent must identify which candidate actions are physically executable in the current state. Existing approaches use supervised fine-tuning (SFT) to train action …

Physics-Informed Neuro-Symbolic Recommender System: A Dual-Physics Approach for Personalized Nutrition

Chayan Banerjee

Traditional e-commerce recommender systems primarily optimize for user engagement and purchase likelihood, often neglecting the rigid physiological constraints required for human health. Standard collaborative filtering algorithms are structurally blind to these hard limits, frequently suggesting bundles that fail to meet specific total …

Physics-Informed Neuro-Symbolic Recommender System: A Dual-Physics Approach for Personalized Nutrition

Chayan Banerjee

Traditional e-commerce recommender systems primarily optimize for user engagement and purchase likelihood, often neglecting the rigid physiological constraints required for human health. Standard collaborative filtering algorithms are structurally blind to these hard limits, frequently suggesting bundles that fail to meet specific total …

au (code pays fourni par la source)

Enhancing Exploration in Actor-Critic Algorithms:An Approach to Incentivize Plausible Novel States

Chayan Banerjee, Zhiyong Chen, Nasimul Noman

Actor-critic (AC) algorithms are model-free deep reinforcement learning techniques that have consistently demonstrated effectiveness across various domains. Enhancing exploration (action entropy) and exploitation (expected return) through more efficient sample utilization is pivotal to their success. A key strategy for a learning algorithm …

au (code pays fourni par la source)

Adaptive Physics-Informed Machine Learning for Bus Travel Time Prediction: Solving the Peak Prediction Problem

Atmadip Goswami, Chayan Banerjee, Dhivya Bharathi, Lelitha Devi Vanajkshi

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Enhancing Exploration in Actor-Critic Algorithms: An Approach to Incentivize Plausible Novel States

Chayan Banerjee, Zhiyong Chen, Nasimul Noman

Actor-critic (AC) algorithms are model-free deep reinforcement learning techniques that have consistently demonstrated effectiveness across various domains. Enhancing exploration (action entropy) and exploitation (expected return) through more efficient sample utilization is pivotal to their success. A key strategy for a learning algorithm …

au (code pays fourni par la source)

Physics-Informed Operator Learning for Hemodynamic Modeling

Ryan Chappell, Chayan Banerjee, Kien Trung Nguyen, Clinton Fookes

Accurate modeling of personalized cardiovascular dynamics is crucial for non-invasive monitoring and therapy planning. State-of-the-art physics-informed neural network (PINN) approaches employ deep, multi-branch architectures with adversarial or contrastive objectives to enforce partial differential equation constraints. While effective, these enhancements introduce significant training …

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LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems

Aayush Agrawal, Ashmitha Jaysi Sivakumar, Ibrahim Kaif, Chayan Banerjee

Abstract Accurate lane segmentation is essential for effective path planning and lane following in autonomous driving, especially in scenarios with significant occlusion from vehicles and pedestrians. Existing models often struggle under such conditions, leading to unreliable navigation and safety risks. We propose …

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