Pedestrian simulation as multi-objective reinforcement learning

• Modelling and simulation of pedestrian crowds require agents to reach pre-determined goals and avoid collisions with static obstacles and dynamic pedestrians, while maintaining natural gait behaviour. We model pedestrians as autonomous, learning, and reactive agents employing Reinforcement Learning (RL). Typical RL-based agent simulations suffer poor generalization due to handcrafted reward function to ensure realistic behaviour. In this work, we model pedestrians in a modular framework integrating navigation and collision-avoidance tasks as separate modules. Each such module consists of independent state-spaces and rewards, but with shared action-spaces. Empirical results suggest that such modular framework learning models can show satisfactory performance without tuning parameters, and we compare it with the state-of-art crowd simulation methods.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Annan teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies (hsv//eng)

Nyckelord

Agent-based simulation

Multi-objective learning

Parallel learning

Reinforcement learning

Publikations- och innehållstyp

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