Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay

Kernkonzepte

A dual generative replay framework that leverages diffusion models to model states and behaviors with high fidelity, allowing the continual learning policy to inherit the distributional expressivity and mitigate catastrophic forgetting.

Zusammenfassung

The paper proposes an efficient Continual learning method via diffusion-based dual Generative Replay for Offline RL (CuGRO) to address the challenges in continual offline reinforcement learning (CORL).

Key highlights:

1. CuGRO decouples the continual learning policy into an expressive generative behavior model and an action evaluation model, allowing the policy to inherit distributional expressivity for encompassing a progressive range of diverse behaviors.
2. CuGRO introduces a state generative model to mimic previous state distributions conditioned on task identity, and pairs the generated states with corresponding responses from the behavior generative model to represent old tasks.
3. CuGRO leverages diffusion probabilistic models to model states and corresponding behaviors with high fidelity, enabling efficient generative replay without storing past samples.
4. CuGRO interleaves replayed samples with real ones of the new task to continually update the state and behavior generators, and uses a multi-head critic with behavior cloning to mitigate forgetting.
5. Experiments demonstrate that CuGRO achieves better forward transfer with less forgetting, and closely approximates the results of using previous ground-truth data due to its high-fidelity replay of the sample space.

Statistiken

"We assume that the task follows a distribution Mk = (S, A, P, r, γ)k ∼P(M). The learner is presented with an infinite sequence of tasks [M1, ..., Mk, ...], and for each task Mk, an offline dataset Dµk = P
i(si, ai, ri, s′
i)k is collected by a behavior policy µk."
"The objective for CORL is to learn a continual policy that maximizes the expected return over all encountered tasks as J(πcontinual) = P
K
k=1 JMk(πMk)."

Zitate

"To the best of our knowledge, CuGRO is the first that leverages expressive diffusion models to tackle the understudied CORL challenge."
"Diffusion probabilistic models [27, 30] are utilized to fit the behavior distribution from the offline dataset."
"We leverage existing advances in diffusion probabilistic models [27] to model states and corresponding behaviors with high fidelity, allowing the continual policy to inherit the distributional expressivity."

Wichtige Erkenntnisse aus

Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay

by Jinmei Liu,W... um arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10662.pdf

Tiefere Fragen

How can the state and behavior generators be further improved to achieve even higher-fidelity replay, such as by incorporating classifier guidance or using a unified diffusion model

To further improve the state and behavior generators for even higher-fidelity replay, several strategies can be considered.

Incorporating Classifier Guidance: One approach is to leverage classifier guidance to enhance the quality of the generated samples. By training the generators with gradients from a pre-trained classifier, the generators can focus on producing samples that align with the classification boundaries learned by the classifier. This can help trade off diversity for fidelity, ensuring that the generated samples are more representative of the true data distribution.

Unified Diffusion Model: Another strategy is to explore the use of a unified diffusion model for both state and behavior generation. By combining the modeling of states and behaviors into a single diffusion model, the network can learn more complex relationships between states and actions, leading to more accurate and high-fidelity sample generation. This unified approach can potentially improve the overall performance of the generators and enhance the quality of the replayed samples.

By incorporating these enhancements, the state and behavior generators can achieve even higher-fidelity replay, capturing the nuances of the data distribution more accurately and improving the overall performance of the continual offline RL framework.

What are the potential limitations of the current multi-head critic approach, and how can it be extended to handle an unbounded number of tasks more efficiently

The current multi-head critic approach, while effective, may have some limitations when handling an unbounded number of tasks.

Memory and Computational Complexity: As the number of tasks increases, the multi-head critic approach may become computationally expensive and memory-intensive. Each additional task requires the creation of a new head in the critic network, leading to increased model complexity and resource requirements.

Task Interference: With an unbounded number of tasks, there is a risk of task interference, where the performance on newer tasks may be impacted by the presence of a large number of previous tasks. The multi-head critic may struggle to effectively manage the knowledge from a growing number of tasks, potentially leading to performance degradation.

To address these limitations and improve the scalability of the multi-head critic approach, several extensions can be considered:

Dynamic Head Allocation: Implementing a dynamic head allocation mechanism that allocates resources based on the importance or relevance of each task. This adaptive approach can help manage computational resources efficiently and prioritize tasks based on their impact on the learning process.

Regularization Techniques: Introducing regularization techniques to prevent catastrophic forgetting and ensure that the multi-head critic maintains a balance between old and new tasks. Techniques like elastic weight consolidation (EWC) or synaptic intelligence can help stabilize the learning process and mitigate interference between tasks.

By addressing these limitations and extending the multi-head critic approach with adaptive mechanisms and regularization techniques, the framework can handle an unbounded number of tasks more efficiently and effectively.

Given the promising results of leveraging diffusion models for continual offline RL, how can this framework be adapted to other continual learning settings beyond RL, such as supervised or unsupervised learning tasks

The success of leveraging diffusion models for continual offline RL opens up possibilities for adapting this framework to other continual learning settings beyond RL, such as supervised or unsupervised learning tasks.

Supervised Learning: In supervised learning tasks, the diffusion-based dual generative replay framework can be applied to mimic the distribution of input-output pairs from past tasks. By training the generators to model the data distribution of previous supervised tasks, the framework can facilitate forward transfer and mitigate forgetting when learning new tasks. This approach can be particularly useful in scenarios where access to real data from previous tasks is limited or costly.

Unsupervised Learning: For unsupervised learning tasks, the diffusion models can be utilized to model the underlying data distribution of past tasks without explicit labels. By generating high-fidelity samples that capture the structure of the data space, the framework can enable continual learning in unsupervised settings, allowing the model to adapt to new data distributions while retaining knowledge from previous tasks.

By adapting the diffusion-based dual generative replay framework to supervised and unsupervised learning tasks, the benefits of high-fidelity replay, forward transfer, and mitigation of forgetting can be extended to a broader range of continual learning scenarios.