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Found 4 papers, 1 papers with code
Vision Transformer for Learning Driving Policies in Complex Multi-Agent Environments
Driving in a complex urban environment is a difficult task that requires a complex decision policy.
MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization
We propose two novel ways of integrating information across agents and time in MACRPO: First, we use a recurrent layer in critic's network architecture and propose a new framework to use a meta-trajectory to train the recurrent layer.
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning
To do this, we train an encoder-decoder deep neural network to predict multiple application-relevant factors such as the trajectories of other agents and the ego car.
Efficient Latent Representations using Multiple Tasks for Autonomous Driving
Driving in the dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision policy.
Robotics
