Stabilizing High Update Ratios and Enhancing Data Efficiency in RL
Recent advancements in reinforcement learning (RL) have focused on improving sample efficiency and training stability, particularly in high update-to-data (UTD) scenarios. Innovations in model-augmented data and adaptive data collection strategies have shown significant promise. Model-augmented data methods, such as those leveraging learned world models to generate supplementary data, have been effective in stabilizing high UTD training, reducing value overestimation, and enhancing continued learning stability. These approaches address the core issue of value function generalization to unobserved actions, a critical challenge in sample-limited environments.
Adaptive data collection strategies, on the other hand, have demonstrated the sub-optimality of fixed-length trajectory schedules in Monte Carlo policy evaluation. By dynamically adjusting trajectory lengths based on error estimation, these methods optimize the allocation of interaction budgets, leading to more accurate final estimates. This adaptive approach not only improves data efficiency but also ensures that more sampling is directed towards critical timesteps where higher accuracy is needed.
In the realm of continuous control, Euclidean data augmentation has emerged as a powerful technique for state-based RL. By transforming state features based on Euclidean symmetries, this method significantly enhances both data efficiency and asymptotic performance, particularly in tasks involving raw kinematic and task features. This approach underscores the importance of feature engineering in RL, advocating for state representations that are amenable to such transformations.
Noteworthy Developments:
- Model-Augmented Data for Temporal Difference learning (MAD-TD): Demonstrates significant stability gains in high UTD RL by using generated data from a learned world model.
- Robust and Iterative Data collection strategy Optimization (RIDO): Adapts trajectory schedules to minimize estimator error, showing superior performance in adaptive data collection.
- Novelty-guided Sample Reuse (NSR): Maximizes sample use by prioritizing updates for novel states, improving convergence rates in continuous control tasks.
