Tackling Long-Horizon Tasks with Model-based Offline Reinforcement Learning

Tackling Long-Horizon Tasks with Model-based Offline Reinforcement Learning

This paper by Kwanyoung Park and Youngwoon Lee introduces a novel approach to address a critical challenge in model-based offline reinforcement learning (RL) pertaining to long-horizon tasks. The primary contribution of this work is the Lower Expectile Q-learning (LEQ) method, which effectively mitigates high bias in value estimation resulting from model rollouts through expectile regression of $\lambda$-returns. This method demonstrates significant improvements over existing techniques, particularly in the context of D4RL AntMaze tasks.

Background

In offline RL, where the learning is restricted to static, pre-collected datasets without further environment interaction, the issue of value overestimation for out-of-distribution actions is prevalent. Existing model-based offline RL methods generate imaginary trajectories using learned models to augment the training data. These approaches have shown success in short-horizon tasks but struggle with long-horizon tasks due to noisy model predictions and value estimations. The current strategies typically involve penalizing the value estimations based on model uncertainty, which, while preventing the exploitation of erroneous values, can lead to suboptimal policies in long-horizon environments.

Lower Expectile Q-learning (LEQ) Approach

LEQ is designed to enhance the performance of model-based offline RL in long-horizon tasks by using expectile regression with a small $\tau$. This technique provides a conservative estimate of the Q-values, addressing the overestimation issue more reliably compared to heuristic or computationally intensive uncertainty estimations used by prior methods. LEQ employs a few key innovations:

1. Expectile Regression: Unlike conventional methods, LEQ doesn't rely on estimating the entire Q-value distribution. Instead, it utilizes expectile regression on sampled Q-values, which simplifies the computation and improves efficiency.
2. $\lambda$-Returns for Long-Horizon Tasks: LEQ leverages multi-step returns, specifically $\lambda$-returns, in its Q-learning and policy optimization processes. This approach reduces biases in the value estimates, providing more accurate learning signals for the policy, which is crucial in long-horizon tasks where value estimates for nearby states can be similar and often noisy.

The critic is trained using a combination of expectile regression on model-generated data and standard Bellman updates on offline data. This hybrid approach enhances the Q-function's robustness against model prediction errors. For policy optimization, LEQ maximizes the lower expectile of the $\lambda$-returns, thereby effectively learning from conservative yet realistic value estimates.

Empirical Results

The experimental evaluation of LEQ spans various benchmark environments, including D4RL AntMaze tasks, D4RL MuJoCo Gym tasks, and the NeoRL benchmark. The results are noteworthy:

• AntMaze Tasks: LEQ significantly outperforms previous methods, achieving success rates that either match or surpass state-of-the-art model-free RL methods. For example, LEQ scores around 58.6 and 60.2 in the antmaze-large-play and antmaze-large-diverse tasks, respectively, far exceeding the near-zero scores of methods like RAMBO.
• MuJoCo Gym Tasks: LEQ consistently performs well, often comparable to the best scores achieved by prior methods across multiple tasks. This highlights its versatility and robustness beyond just long-horizon challenges.

Implications and Future Directions

From a practical standpoint, LEQ represents a significant step forward in the ability of RL systems to perform reliably in scenarios where data is limited to static datasets, particularly for long-horizon tasks. This has notable applications in fields like robotics and autonomous systems, where real-world interactions are expensive or impractical during the training phase.

Theoretically, LEQ's use of expectile regression for conservative value estimation introduces a new paradigm in model-based RL that could inspire further research. Future work could explore the applicability of LEQ in more complex environments, including those with high-dimensional observations, or extend its principles to design new algorithms that address other limitations inherent in offline RL.

By effectively handling long-horizon task challenges through a combination of expectile regression and $\lambda$-returns, LEQ sets a new benchmark and opens up pathways for more reliable and efficient model-based offline RL in diverse applications.