AI Policy Representation and Optimization: A Closer Look
Recent studies have shown that the way a policy is represented can impact learning performance significantly. Previous research has explored various policy representations such as feed-forward neural networks, energy-based models, and diffusion.
Optimizing Actions with High-Dimensional Data
A study conducted by researchers from Carnegie Mellon University and Peking University introduces a unique approach to producing actions for deep reinforcement and imitation learning. This approach involves using high-dimensional sensory data like images and point clouds, along with differentiable trajectory optimization as the policy representation. By defining cost and dynamics functions, trajectory optimization helps in determining the actions to be taken based on input states.
Solving the “Objective Mismatch” Problem
The innovative approach, known as DiffTOP (Differentiable Trajectory Optimization), aims to address the “objective mismatch” problem present in current model-based RL algorithms. By optimizing trajectories and back-propagating policy gradient loss, DiffTOP enhances task performance by improving both latent dynamics and reward models.
Outperforming State-of-the-Art Methods
Extensive experiments have demonstrated that DiffTOP surpasses previous state-of-the-art methods in both model-based RL and imitation learning tasks. Tasks involving high-dimensional sensory observations, such as Robomimic tasks with images and Maniskill1 and Maniskill2 challenges with point clouds, were completed successfully using DiffTOP.
This hybrid approach offers improved performance compared to feed-forward policy classes, Energy-Based Models (EBM), and Diffusion. By optimizing trajectories and learning cost functions during testing, DiffTOP surpasses existing alternatives and avoids training instability.
To learn more about this research, read the full paper.
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