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Found 3 papers, 0 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.
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset.
End-to-End Velocity Estimation For Autonomous Racing
Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip.
