66,710 research outputs found
Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations
Autonomous driving involves complex decision-making in highly interactive
environments, requiring thoughtful negotiation with other traffic participants.
While reinforcement learning provides a way to learn such interaction behavior,
efficient learning critically depends on scalable state representations.
Contrary to imitation learning methods, high-dimensional state representations
still constitute a major bottleneck for deep reinforcement learning methods in
autonomous driving. In this paper, we study the challenges of constructing
bird's-eye-view representations for autonomous driving and propose a recurrent
learning architecture for long-horizon driving. Our PPO-based approach, called
RecurrDriveNet, is demonstrated on a simulated autonomous driving task in
CARLA, where it outperforms traditional frame-stacking methods while only
requiring one million experiences for efficient training. RecurrDriveNet causes
less than one infraction per driven kilometer by interacting safely with other
road users.Comment: IEEE International Conference on Intelligent Transportation Systems
202
Graphical Object-Centric Actor-Critic
There have recently been significant advances in the problem of unsupervised
object-centric representation learning and its application to downstream tasks.
The latest works support the argument that employing disentangled object
representations in image-based object-centric reinforcement learning tasks
facilitates policy learning. We propose a novel object-centric reinforcement
learning algorithm combining actor-critic and model-based approaches to utilize
these representations effectively. In our approach, we use a transformer
encoder to extract object representations and graph neural networks to
approximate the dynamics of an environment. The proposed method fills a
research gap in developing efficient object-centric world models for
reinforcement learning settings that can be used for environments with discrete
or continuous action spaces. Our algorithm performs better in a visually
complex 3D robotic environment and a 2D environment with compositional
structure than the state-of-the-art model-free actor-critic algorithm built
upon transformer architecture and the state-of-the-art monolithic model-based
algorithm
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