Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning

Abstract#

We propose PTGM (Pre-Training Goal-based Models), a pre-training approach for goal-based models that achieves sample-efficient reinforcement learning. Our method significantly reduces the sample complexity of RL algorithms through effective pre-training strategies that leverage goal-conditioned learning.

Introduction#

Sample efficiency remains one of the most significant challenges in reinforcement learning, particularly when deploying algorithms in real-world scenarios where data collection is expensive and time-consuming. Traditional RL methods often require millions of interactions with the environment to learn effective policies.

Method#

PTGM leverages goal-conditioned pre-training to learn useful representations that transfer to downstream RL tasks. Our approach consists of:

Pre-training Phase#

1. Goal-conditioned Representation Learning: Learn to predict future states given current state and goal
2. Behavioral Cloning on Goal-reaching: Pre-train on diverse goal-reaching behaviors
3. Self-supervised Learning: Use various self-supervised objectives to learn robust features

Fine-tuning Phase#

1. Policy Initialization: Initialize policy networks with pre-trained representations
2. Efficient Exploration: Use learned representations to guide exploration
3. Transfer Learning: Adapt pre-trained models to specific downstream tasks

Key Innovations#

• Goal-conditioned Pre-training: Novel pre-training objectives specifically designed for goal-based learning
• Representation Transfer: Effective transfer of learned representations across different tasks
• Sample Efficiency: Significant reduction in sample requirements for downstream tasks

Experimental Results#

Our experiments demonstrate substantial improvements over baseline methods across various environments:

Continuous Control Tasks#

• Manipulation: 3x improvement in sample efficiency on robotic manipulation tasks
• Navigation: 2.5x faster convergence on navigation benchmarks
• Locomotion: Consistent improvements across different locomotion tasks

Discrete Environments#

• Maze Navigation: Superior performance on complex maze environments
• Grid Worlds: Faster learning on various grid-world tasks

Results Summary#

PTGM achieves:

• 50-70% reduction in sample complexity across evaluated tasks
• Consistent improvements over strong baseline methods
• Robust transfer across different environment types

Theoretical Analysis#

We provide theoretical justification for our approach, showing that goal-conditioned pre-training leads to better representation learning and faster convergence guarantees.

Future Directions#

• Extension to multi-task and meta-learning settings
• Application to real-world robotics scenarios
• Integration with large-scale foundation models

Citation#

@inproceedings{yuan2024ptgm,
  title={Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning},
  author={Yuan, Haoqi and Mu, Zhancun and Xie, Feiyang and Lu, Zongqing},
  booktitle={International Conference on Learning Representations},
  year={2024},
  note={Oral Presentation}
}