Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions (AI summary)

Key Points

1. The paper introduces Q-Transformer as a scalable reinforcement learning method for training multi-task policies using large offline datasets incorporating both human demonstrations and autonomously collected data.

2. Q-Transformer utilizes a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups, allowing effective high-capacity sequence modeling techniques for Q-learning.

3. The method discretizes each action dimension and represents the Q-value of each action dimension as separate tokens, allowing for effective incorporation of high-capacity expressive models in robotic learning.

4. Design decisions enable Q-Transformer to outperform prior offline reinforcement learning algorithms and imitation learning techniques on diverse real-world robotic manipulation tasks, as demonstrated in large-scale real-world experiments and simulations.

5. The approach addresses challenges associated with training high-capacity models such as Transformers using reinforcement learning algorithms and focuses on methods that can effectively train such models to represent Q-values.

6. The paper introduces new modeling decisions for autoregressive Q-learning update to treat each action dimension as a separate time step for effective offline reinforcement learning on real-world robotic manipulation tasks.

7. The method employs a specific conservative regularizer to learn from offline datasets and also utilizes hybrid update combining Monte Carlo and n-step returns with temporal difference backups to improve performance.

8. Experimental evaluation validates the Q-Transformer by learning large-scale text-conditioned multi-task policies, both in simulation for rigorous comparisons and in large-scale real-world experiments for realistic validation.

9. The paper acknowledges limitations and future directions, including the focus on sparse binary reward tasks, potential challenges with higher-dimensional action spaces, and potential extensions of Q-Transformer to online fine-tuning.

Summary

The paper introduces Q-Transformer, a scalable reinforcement learning method for training multi-task policies using large offline datasets, which can leverage both human demonstrations and autonomously collected data. The method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups, enabling effective high-capacity sequence modeling techniques for Q-learning. Several design decisions enable good performance with offline RL training, and Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The method addresses the limitations of existing robotic learning methods, such as the reliance on supervised learning and the inability to effectively train large-scale models using RL algorithms.
<b> Per-dimension Tokenization and Application to Robotic Datasets</b>
The authors propose a per-dimension tokenization of Q-values and demonstrate how Q-Transformer can be readily applied to large and diverse robotic datasets, including real-world data. They also discuss the challenges and limitations of the approach, such as the need for adaptive discretization methods for high-dimensional action spaces and the focus on sparse binary reward tasks. Additionally, they consider scalability to extremely large dataset sizes, demonstrating the ability of Q-Transformer to improve even with a very high number of successful demonstrations.
<b> Experimental Evaluations of Q-Transformer</b>
The paper includes extensive experimental evaluations on real-world robotic manipulation tasks and simulated offline RL tasks, demonstrating the effectiveness and generalizability of Q-Transformer. The paper also discusses the importance of specific design choices and methodological improvements, such as the exploitation of noisy data for policy improvement and the use of n-step returns to accelerate Q-learning. Overall, the results show that Q-Transformer is a promising approach for large-scale robotic reinforcement learning and can effectively learn from a combination of demonstrations and autonomous data.

Reference: https://arxiv.org/abs/2309.10150