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Found 6 papers, 1 papers with code
Hierarchical Imitation Learning for Stochastic Environments
However, such methods are often inappropriate for stochastic environments where the agent must also react to external factors: because agent types are inferred from the observed future trajectory during training, these environments require that the contributions of internal and external factors to the agent behaviour are disentangled and only internal factors, i. e., those under the agent's control, are encoded in the type.
The Waymo Open Sim Agents Challenge
Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development.
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency.
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator.
