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Found 3 papers, 3 papers with code
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning
Insufficient learning (due to convergence to local optima) results in under-performing policies whilst redundant learning wastes time and resources.
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements.
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
Our work demonstrates that invariant transformations on RL trajectories are a promising methodology to speed up learning in deep RL.
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