ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
Gpudrive: Data-driven, multi-agent driving simulation at 1 million fps
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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ScenarioControl introduces the first vision-language controllable generator for realistic vectorized 3D driving scenarios with temporal consistency across actor views.
FAST uses termination-rate-triggered virtual continuation plus masked normalized PPO loss to cut parallel RL sampling latency by ≥1.78× without biasing autonomous-driving policies.
SceneFactory delivers a batched GPU platform for physics-based multi-agent autonomous driving simulation that achieves 127x higher throughput than non-vectorized PhysX while supporting articulated dynamics and road-condition friction.
Self-play RL regularized with 30 minutes of human data produces driving policies that coordinate with humans, training in 15 hours on one GPU with 2500x less data than imitation learning.
An instance-centric representation with local frames, relative positional encodings, and adaptive reward transformation in adversarial IRL yields scalable, accurate, and robust behavior models for multi-agent driving simulation.
Gymnasium establishes a standardized API for RL environments to improve interoperability, reproducibility, and ease of development in reinforcement learning.
citing papers explorer
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Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
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ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation
ScenarioControl introduces the first vision-language controllable generator for realistic vectorized 3D driving scenarios with temporal consistency across actor views.
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FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving
FAST uses termination-rate-triggered virtual continuation plus masked normalized PPO loss to cut parallel RL sampling latency by ≥1.78× without biasing autonomous-driving policies.
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SceneFactory: GPU-Accelerated Multi-Agent Driving Simulation with Physics-Based Vehicle Dynamics
SceneFactory delivers a batched GPU platform for physics-based multi-agent autonomous driving simulation that achieves 127x higher throughput than non-vectorized PhysX while supporting articulated dynamics and road-condition friction.
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Human-like autonomy emerges from self-play and a pinch of human data
Self-play RL regularized with 30 minutes of human data produces driving policies that coordinate with humans, training in 15 hours on one GPU with 2500x less data than imitation learning.
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Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation
An instance-centric representation with local frames, relative positional encodings, and adaptive reward transformation in adversarial IRL yields scalable, accurate, and robust behavior models for multi-agent driving simulation.
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Gymnasium: A Standard Interface for Reinforcement Learning Environments
Gymnasium establishes a standardized API for RL environments to improve interoperability, reproducibility, and ease of development in reinforcement learning.