Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
This work addresses the reality gap for robotics and autonomous systems, offering a more efficient solution for domain adaptation, though it is incremental as it builds on existing attention and policy optimization techniques.
The paper tackles the problem of simulation-to-real transfer in reinforcement learning by proposing a deep attention convolutional neural network (DACNN) that achieves zero-shot learning without pre-training, demonstrating performance comparable to methods requiring high computational complexity in autonomous driving scenarios.
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem. To close the reality gap, prior methods to simulation-to-real world transfer focused on domain adaptation, decoupling perception and dynamics and solving each problem separately, and randomization of agent parameters and environment conditions to expose the learning agent to a variety of conditions. While these methods provide acceptable performance, the computational complexity required to capture a large variation of parameters for comprehensive scenarios on a given task such as autonomous driving or robotic manipulation is high. Our key contribution is to theoretically prove and empirically demonstrate that a deep attention convolutional neural network (DACNN) with specific visual sensor configuration performs as well as training on a dataset with high domain and parameter variation at lower computational complexity. Specifically, the attention network weights are learned through policy optimization to focus on local dependencies that lead to optimal actions, and does not require tuning in real-world for generalization. Our new architecture adapts perception with respect to the control objective, resulting in zero-shot learning without pre-training a perception network. To measure the impact of our new deep network architecture on domain adaptation, we consider autonomous driving as a use case. We perform an extensive set of experiments in simulation-to-simulation and simulation-to-real scenarios to compare our approach to several baselines including the current state-of-art models.