Continual Offline Reinforcement Learning with Decision Transformer: Addressing Catastrophic Forgetting

Core Concepts

Decision Transformer can serve as a more suitable offline continuous learner compared to Actor-Critic based algorithms, but faces challenges with catastrophic forgetting. The proposed methods MH-DT and LoRA-DT address this issue by leveraging the transformer structure to store and transfer knowledge across tasks.

Abstract

The content discusses the problem of Continual Offline Reinforcement Learning (CORL), which aims to enable agents to learn multiple tasks from static offline datasets and adapt to new tasks. Existing methods based on Actor-Critic (AC) structures face challenges such as distribution shifts, low efficiency, and limited knowledge-sharing.
The authors propose that Decision Transformer (DT), another offline RL paradigm, can serve as a more suitable offline continuous learner. DT offers advantages in learning efficiency, distribution shift mitigation, and zero-shot generalization, but also exacerbates the forgetting problem during supervised parameter updates.
To address this, the authors introduce two new DT-based methods:

Multi-Head DT (MH-DT): Uses multiple heads to store task-specific knowledge and share knowledge with common components. Employs distillation and selective rehearsal to enhance current task learning.

Low-Rank Adaptation DT (LoRA-DT): Merges less influential weights and fine-tunes the MLP layer in DT blocks using LoRA to adapt to the current task, without requiring a replay buffer.

Experiments on MuJoCo and Meta-World benchmarks demonstrate that the proposed methods outperform SOTA CORL baselines, showcase enhanced learning capabilities, and are more memory-efficient.

Stats

Offline datasets from Ant-Dir, Walker-Par, Cheetah-Vel, and Meta-World reach-v2 environments are used.
The target speed of the cheetah in T1 to T6 tasks keeps increasing, making the tasks more difficult.
Each task is most similar to the tasks adjacent to it in sequence.

Quotes

"Decision Transformer (DT) (Chen et al., 2021), another offline RL paradigm, shows extremely strong learning efficiency and can ignore the problem of distribution shift in offline RL because its supervised learning training method."
"We aim to investigate whether DT can serve as a more suitable offline continuous learner in this work."
"Inspired by DT's outstanding characteristics and previous multi-task work, we aim to investigate whether DT can serve as a more suitable offline continuous learner in this work."

Key Insights Distilled From

Solving Continual Offline Reinforcement Learning with Decision Transformer

by Kaixin Huang... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2401.08478.pdf

Deeper Inquiries

How can the proposed methods be extended to handle more diverse and unrelated tasks in the continual learning setting

The proposed methods, MH-DT and LoRA-DT, can be extended to handle more diverse and unrelated tasks in the continual learning setting by incorporating more sophisticated mechanisms for knowledge transfer and retention. One approach could be to introduce a meta-learning component that adapts the model's learning strategy based on the characteristics of each new task. This meta-learning component could help the model quickly identify task similarities and differences, allowing for more efficient knowledge transfer. Additionally, incorporating techniques like domain adaptation or transfer learning could help the model generalize better to new and diverse tasks by leveraging knowledge from previously learned tasks. By enhancing the model's ability to adapt to a wide range of tasks, it can improve its performance in handling diverse and unrelated tasks in the continual learning setting.

What are the potential limitations of the DT-based approaches, and how can they be addressed to further improve performance

The potential limitations of DT-based approaches include the risk of catastrophic forgetting, especially when faced with a large number of tasks or significant distribution shifts between tasks. To address this limitation and further improve performance, several strategies can be implemented. One approach is to explore more advanced regularization techniques, such as elastic weight consolidation (EWC) or synaptic intelligence (SI), to protect important parameters related to previous tasks during learning new tasks. Additionally, incorporating techniques like experience replay or distillation can help mitigate forgetting by selectively reinforcing important knowledge while learning new tasks. Furthermore, exploring ensemble methods or model distillation approaches can enhance the model's robustness and generalization capabilities, reducing the impact of forgetting on performance.

What other offline RL algorithms or architectures could be explored as alternatives to DT for continual learning, and how would their strengths and weaknesses compare to the DT-based methods

Other offline RL algorithms or architectures that could be explored as alternatives to DT for continual learning include Actor-Critic (AC) structures, model-based approaches, and reinforcement learning with dynamic models. AC structures offer a well-established framework for reinforcement learning and can be adapted for continual learning by incorporating rehearsal-based methods or regularization techniques. Model-based approaches leverage supervised learning to train a dynamic model and mitigate out-of-distribution samples, providing a different perspective on handling continual learning tasks. Reinforcement learning with dynamic models can offer a unique approach to long-term credit assignment and stability in learning multiple tasks. Each of these alternatives has its strengths and weaknesses compared to DT-based methods. AC structures provide a strong foundation for policy learning but may struggle with catastrophic forgetting. Model-based approaches offer robustness to distribution shifts but may require more data for training. Reinforcement learning with dynamic models can provide efficient credit assignment but may face challenges in scalability and complexity. By exploring these alternatives, researchers can gain a comprehensive understanding of the best approaches for continual learning in offline reinforcement learning scenarios.