Active Reinforcement Learning Strategies for Offline Policy Improvement (2412.13106v2)

Abstract: Learning agents that excel at sequential decision-making tasks must continuously resolve the problem of exploration and exploitation for optimal learning. However, such interactions with the environment online might be prohibitively expensive and may involve some constraints, such as a limited budget for agent-environment interactions and restricted exploration in certain regions of the state space. Examples include selecting candidates for medical trials and training agents in complex navigation environments. This problem necessitates the study of active reinforcement learning strategies that collect minimal additional experience trajectories by reusing existing offline data previously collected by some unknown behavior policy. In this work, we propose an active reinforcement learning method capable of collecting trajectories that can augment existing offline data. With extensive experimentation, we demonstrate that our proposed method reduces additional online interaction with the environment by up to 75% over competitive baselines across various continuous control environments such as Gym-MuJoCo locomotion environments as well as Maze2d, AntMaze, CARLA and IsaacSimGo1. To the best of our knowledge, this is the first work that addresses the active learning problem in the context of sequential decision-making and reinforcement learning.

• The paper proposes a novel representation-aware, uncertainty-based active trajectory collection method that uses offline data to efficiently select interactions.
• The method demonstrates compatibility with various offline RL algorithms and reduces online interactions by up to 75% compared to baselines.
• Experiments across diverse continuous control tasks show the method outperforms standard fine-tuning, proving effective for high-cost interaction scenarios.

Active Reinforcement Learning Strategies for Offline Policy Improvement

The paper explores the intersection of offline reinforcement learning (RL) and active exploration strategies to efficiently improve policy performance under constrained interaction budgets with the environment. The authors address a critical challenge in reinforcement learning: collecting informative data for policy improvement when interacting with the environment is prohibitively expensive or restricted, as is often the case in real-world applications like clinical trials and autonomous driving.

The core contribution of the paper is a novel representation-aware, uncertainty-based active trajectory collection method. This method intelligently decides which interactions with the environment are necessary, utilizing existing offline data to minimize additional data collection. By estimating the uncertainty in regions of the state space using a model ensemble approach, the method effectively identifies where additional data collection would be most informative. The ensemble's epistemic uncertainty quantifies model confidence and guides both the selection of initial states and the trajectory collection strategy.

Key Contributions and Methodology

1. Epistemic Uncertainty Estimation: The paper proposes learning an ensemble of representation models to capture epistemic uncertainty effectively. These models are trained with an augmented noise-contrastive loss that aligns state and action representations to reflect both clustering and transition dynamics objectives.
2. Active Trajectory Collection: The trajectory collection is framed as an active exploration strategy where candidate initial states are assessed for uncertainty, and the most uncertain are chosen for further exploration. The action selection during exploration is refined using an $\epsilon$-greedy policy that balances exploitation of the current policy and exploration based on model uncertainty.
3. Practical Compatibility and Efficiency: The proposed method demonstrates compatibility with various existing offline RL algorithms such as TD3+BC, IQL, and CQL. It reduces the need for online interactions by up to 75% compared to baselines, indicating a significant efficiency improvement.
4. Extensive Experimental Validation: Experiments conducted across diverse continuous control environments, including Maze2D, AntMaze, locomotion tasks, CARLA, and Unitree Go1, show that the proposed method consistently outperforms standard fine-tuning approaches both in terms of reward and sample efficiency.

Implications and Future Directions

The methodology presented is particularly valuable for scenarios where the cost of new interactions is high, and the availability of comprehensive offline data can be leveraged to inform and guide efficient exploration. The strong empirical results suggest that incorporating a representation-based uncertainty estimation into RL systems can considerably enhance data efficiency, paving the way for broader adoption in domains constrained by interaction budgets.

Theoretically, the framework could be expanded to incorporate more sophisticated uncertainty models or alternative representations that could further refine the trajectory selection strategy. Practically, adapting this framework to support larger scale or more heterogeneous datasets could enhance its utility in even more complex real-world applications.

Future work could also explore dynamic adaptation of the exploration-exploitation balance, refining how the method decides between trajectory termination and initial state selection as more data is collected. Another potential avenue is integrating this active exploration strategy with policy or model learning paradigms that specifically target safety or robustness, broadening its applicability to risk-sensitive environments.

In conclusion, this paper adds a significant piece to the reinforcement learning landscape, proposing a method that not only conserves interaction budgets but also strategically improves policy learning by focusing on data quality and informativeness. Its approach sets a promising precedent for future research aimed at maximizing learning efficiency with minimal environmental interaction.