Accelerating exploration and representation learning with offline pre-training (2304.00046v1)

Abstract: Sequential decision-making agents struggle with long horizon tasks, since solving them requires multi-step reasoning. Most reinforcement learning (RL) algorithms address this challenge by improved credit assignment, introducing memory capability, altering the agent's intrinsic motivation (i.e. exploration) or its worldview (i.e. knowledge representation). Many of these components could be learned from offline data. In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset. We show that learning a state representation using noise-contrastive estimation and a model of auxiliary reward separately from a single collection of human demonstrations can significantly improve the sample efficiency on the challenging NetHack benchmark. We also ablate various components of our experimental setting and highlight crucial insights.

• The paper demonstrates that offline pre-training significantly enhances exploration and representation learning by leveraging noise-contrastive estimation and auxiliary progress rewards.
• The approach improves sample efficiency on the complex NetHack benchmark compared to baseline imitation learning strategies.
• The study finds that a ResNet-based encoder outperforms Vision Transformers in capturing the intricate state dynamics of high-dimensional RL tasks.

Accelerating Exploration and Representation Learning with Offline Pre-Training

The paper "Accelerating Exploration and Representation Learning with Offline Pre-Training" presents a detailed investigation into enhancing reinforcement learning (RL) efficacy on long-horizon tasks by integrating offline pre-training strategies. The research is grounded in the hypothesis that the two critical components of RL—exploration and representation learning—can be significantly bolstered by leveraging offline data to train two distinct models separately but from the same dataset.

Key Contributions

The authors propose using offline pre-training to refine state representations and auxiliary reward models from human demonstrations, showcasing their work on the challenging NetHack benchmark. The key contributions of this research can be summarized as follows:

1. Offline Pre-Training for Representation Learning: The paper advocates for pre-training state representations using noise-contrastive estimation. This method is centered on implicit models that approximate future state visitation probabilities, effectively capturing the dynamics and progressions inherent in the trajectory data.
2. Auxiliary Reward through Progress Models: An auxiliary reward model is developed where a "progress model" is leveraged to provide intrinsic motivation for exploration. Inspired by the ELE (Explore Like Experts) framework, this model rewards the agent based on its temporal advancement towards states that are indicative of expert trajectories.
3. Empirical Evaluation on NetHack: The methodology is rigorously tested using the NetHack Learning Environment. The combination of pre-trained representations and auxiliary progress rewards was demonstrated to notably improve sample efficiency and, consequently, performance across several tasks within NetHack, which is known for its complexity.

Experimental Insights

The experimental results provide several compelling insights:

• Sample Efficiency: Pre-training using contrastive learning resulted in a significant improvement in sample efficiency compared to baseline methods and other imitation learning strategies (such as Behavior Cloning from Observations, GAIfO, and FORM).
• Dense vs. Sparse Rewards: The pre-trained state representations alone were effective in tasks with dense rewards, facilitating faster convergence and achieving higher performance. However, for sparse reward tasks, the addition of the progress model's auxiliary rewards was essential, emphasizing the need for exploration bonuses in challenging environments.
• Contrasting Architectures: Interestingly, architectures mattered. A ResNet-based encoder performed better than Vision Transformers, indicating that ResNet might be better suited for encoding the highly variable and complex states found in NetHack due to its inherent structure.

Theoretical and Practical Implications

Theoretically, the paper underscores the interplay between representation learning and intrinsic motivation, revealing how offline data can be strategically utilized to overcome some of the inherent challenges in RL—particularly those related to exploration in vast state and action spaces. Practically, the findings suggest that combining offline pre-trained models for state representation with auxiliary exploration rewards could provide a potent framework for tackling complex RL tasks.

Future Directions

This work opens several avenues for future exploration:

• Scalability: Investigating the scalability of the approach to other complex and high-dimensional environments outside of NetHack could further validate the methodology.
• Diverse Data Utilization: Exploring the integration of diverse datasets, potentially combining data from different expert sources or synthetic trajectories, may enhance the robustness and generalization capabilities of the resulting models.
• Refinement of Progress Models: Further refinement of progress models could explore adaptive mechanisms that dynamically adjust the auxiliary rewards based on real-time performance metrics.

In conclusion, this research provides a compelling narrative on how offline pre-training can be harnessed to significantly ameliorate the challenges posed by long-horizon, high-dimensional RL tasks. The robust empirical evaluation and the integrative approach combining pre-trained representations with auxiliary rewards delineate a promising pathway for future RL methodologies.