MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL (2410.08896v2)

Abstract: Building deep reinforcement learning (RL) agents that find a good policy with few samples has proven notoriously challenging. To achieve sample efficiency, recent work has explored updating neural networks with large numbers of gradient steps for every new sample. While such high update-to-data (UTD) ratios have shown strong empirical performance, they also introduce instability to the training process. Previous approaches need to rely on periodic neural network parameter resets to address this instability, but restarting the training process is infeasible in many real-world applications and requires tuning the resetting interval. In this paper, we focus on one of the core difficulties of stable training with limited samples: the inability of learned value functions to generalize to unobserved on-policy actions. We mitigate this issue directly by augmenting the off-policy RL training process with a small amount of data generated from a learned world model. Our method, Model-Augmented Data for TD Learning (MAD-TD), uses small amounts of generated data to stabilize high UTD training and achieve competitive performance on the most challenging tasks in the DeepMind control suite. Our experiments further highlight the importance of employing a good model to generate data, MAD-TD's ability to combat value overestimation, and its practical stability gains for continued learning.

• The paper introduces MAD-TD, a novel approach that uses model-generated data to stabilize high update ratio reinforcement learning.
• It integrates model data with real replay data in a DYNA-like framework to mitigate Q function overestimation and reduce network resets.
• Experimental results on DeepMind Control tasks demonstrate that MAD-TD outperforms traditional off-policy methods, offering improved stability and action-value estimation.

MAD-TD: Enhancing High Update Ratio Reinforcement Learning with Model-Augmented Data

The paper "MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL" presents a novel approach to enhance the stability and performance of reinforcement learning (RL) at high update-to-data (UTD) ratios. The authors address the challenges inherent in off-policy RL, particularly the instability caused by large numbers of gradient updates relative to the amount of data. This instability often necessitates network resets, which are impractical in many real-world scenarios. The proposed method, Model-Augmented Data for Temporal Difference learning (MAD-TD), introduces model-generated data to tackle these issues, demonstrating promising results on complex control tasks.

Theoretical Foundations and Core Challenges

The paper builds upon the standard RL setting, specifically targeting the issues of misgeneralization in off-policy value function estimation. At its core, the challenge lies in the inability of learned value functions to accurately generalize to unobserved on-policy actions due to distribution shifts between collected data and target policy actions. The authors argue that previous strategies, including pessimistic underestimation and ensemble methods, do not fully resolve these issues, often requiring costly network resets or presenting other impracticalities.

Methodology

MAD-TD offers a fresh perspective by leveraging a small amount of data generated from a learned world model to supplement the primary RL training process. The idea is straightforward yet impactful: using model-generated data alongside real replay data stabilizes the learning process. The methodology integrates with the TD3 algorithm, adjusting the architecture to interleave model updates in a DYNA-like framework. This approach provides a regularization effect on the action-value estimates under the target policy by correcting for misgeneralization with on-policy data.

Experimental Validation

Experimental results on the DeepMind Control suite, particularly its more demanding humanoid and dog environments, affirm the efficacy of MAD-TD. The method consistently outperformed traditional high UTD approaches that solely rely on real data. Of particular note, the authors highlight how MAD-TD effectively mitigates Q function overestimation, a well-documented issue in high UTD settings, leading to stable learning without the need for ensemble models or network resets.

Implications and Speculation

The introduction of model-based data not only addresses stability issues in high UTD RL but also provides intriguing implications for future AI development. By demonstrating that a minimal amount of model-generated data can correct significant estimation issues, MAD-TD opens the door to further exploration of hybrid model-free/model-based RL strategies. Future research might explore enhancing model accuracy or integrating advanced model-based frameworks such as diffusion models to heighten performance further.

Another potential area of exploration could involve combining MAD-TD with approaches like uncertainty quantification or multi-step corrections, potentially enriching the method's robustness and applicability across various RL tasks.

Conclusion

MAD-TD introduces a compelling solution to stabilize high update ratio reinforcement learning using model-augmented data. By directly targeting misgeneralization to unobserved actions, this method marks an influential step forward in improving RL's reliability in scenarios with limited data. While challenges remain, particularly in scaling and generalizing this approach to various environments, MAD-TD sets a foundational precedent for blending model-free and model-based learning paradigms, potentially catalyzing broader advancements in the field of artificial intelligence.