Synthetic Experience Replay (2303.06614v4)

Abstract: A key theme in the past decade has been that when large neural networks and large datasets combine they can produce remarkable results. In deep reinforcement learning (RL), this paradigm is commonly made possible through experience replay, whereby a dataset of past experiences is used to train a policy or value function. However, unlike in supervised or self-supervised learning, an RL agent has to collect its own data, which is often limited. Thus, it is challenging to reap the benefits of deep learning, and even small neural networks can overfit at the start of training. In this work, we leverage the tremendous recent progress in generative modeling and propose Synthetic Experience Replay (SynthER), a diffusion-based approach to flexibly upsample an agent's collected experience. We show that SynthER is an effective method for training RL agents across offline and online settings, in both proprioceptive and pixel-based environments. In offline settings, we observe drastic improvements when upsampling small offline datasets and see that additional synthetic data also allows us to effectively train larger networks. Furthermore, SynthER enables online agents to train with a much higher update-to-data ratio than before, leading to a significant increase in sample efficiency, without any algorithmic changes. We believe that synthetic training data could open the door to realizing the full potential of deep learning for replay-based RL algorithms from limited data. Finally, we open-source our code at https://github.com/conglu1997/SynthER.

• The paper introduces SynthER, which leverages diffusion models to generate high-fidelity synthetic experiences that improve RL policy performance.
• The method applies to both offline and online RL settings, replacing or augmenting real experiences to boost sample efficiency.
• Empirical results demonstrate that synthetic experience replay rivals traditional augmentation techniques and enables scaling of network architectures.

Synthetic Experience Replay: A Diffusion-Based Approach

In the field of reinforcement learning (RL), experience replay stands as a critical mechanism for leveraging past experiences to train policies and value functions. However, obtaining a vast amount of quality training data is a perennial challenge, particularly when compared to supervised learning where datasets are readily available. The paper "Synthetic Experience Replay" introduces a pioneering method—SynthER—rooted in recent advancements in generative modeling, specifically diffusion models, to tackle this limitation.

Key Contributions and Methodology

SynthER proposes synthetic experience replay using diffusion-based generative models to augment an RL agent's experiences. The approach is not only versatile, applicable to both offline and online RL settings, but it also demonstrates efficacy in both proprioceptive and pixel-based environments.

1. Diffusion Models in RL: SynthER leverages diffusion models to generate synthetic data that mirrors the distribution of real experiences. This approach contrasts with traditional methods such as VAEs or GANs by providing a higher fidelity of generative experiences, as substantiated by superior downstream policy performance.
2. Offline and Online RL Settings: The paper outlines the use of SynthER in offline settings, permitting the replacement of real data entirely with synthetic samples while maintaining performance parity or better across a wide array of environments and RL algorithms. Furthermore, in online settings, SynthER allows for the upsampling of an agent's training data, thereby significantly increasing sample efficiency without necessitating algorithmic changes.
3. Empirical Performance: Rigorous experiments demonstrate SynthER's ability to surpass traditional data augmentation techniques and provide substantial benefits when training on diminished datasets. Moreover, the method supports scaling the size of neural network architectures due to the availability of enhanced synthetic data, hinting at an alleviation of the representational bottleneck typically seen in RL tasks.

Evaluation and Results

The quantitative evaluation presented in the paper is comprehensive, covering multiple algorithms across diverse environments:

• Offline RL: In settings with severely limited datasets, SynthER facilitates the generation of sufficient high-quality synthetic data to rival or exceed baseline performance. For instance, on standard benchmarks such as D4RL, SynthER showcases significant advancements over explicit data augmentation strategies.
• Online RL: By coupling SynthER with a high update-to-data ratio, agents achieve improved sample efficiency, aligning with or outperforming robust RL algorithms like REDQ.

Implications and Future Directions

SynthER significantly contributes to the capabilities of RL, suggesting that diffusion-based models can not only bolster existing algorithms through enhanced data but also open avenues for entirely new training paradigms. This work hints at future explorations, such as the integration of guidance in diffusion processes for selectively biased artificial samples, or the adaptation of diffusion methodologies within model-based RL frameworks for optimized rollouts.

While SynthER provides compelling support for entirely synthesizing experience data, the exploration of scaling to generative models pre-trained on vast out-domain data could further enhance this approach, potentially leading to novel forms of zero-shot learning in RL environments.

In conclusion, "Synthetic Experience Replay" marks a significant stride in the field of reinforcement learning and experience replay by combining cutting-edge generative processes with traditional RL paradigms. Its implications stretch from advancing current RL methodologies to potentially reimagining training strategies in data-constrained scenarios.