Core Concepts
This paper introduces two novel offline reinforcement learning frameworks, RCDTP and RWDTP, which reframe RL problems as regression tasks solvable by decision trees, achieving comparable performance to established methods while offering faster training and inference, and enhanced explainability.
Abstract
Bibliographic Information:
Koirala, P., & Fleming, C. (2024). Solving Offline Reinforcement Learning with Decision Tree Regression. arXiv preprint arXiv:2401.11630v2.
Research Objective:
This paper aims to address the challenges of offline reinforcement learning (RL) by introducing two novel frameworks, Return-Conditioned Decision Tree Policy (RCDTP) and Return-Weighted Decision Tree Policy (RWDTP), that leverage decision tree regression for efficient and explainable policy learning.
Methodology:
The authors reframe the offline RL problem as a supervised regression task, utilizing decision trees as function approximators. They introduce RCDTP, which conditions actions on state, return-to-go, and timestep, and RWDTP, which conditions actions on state and a return-weighted factor. Both frameworks are trained using the XGBoost algorithm, optimizing for minimal regression error. The authors evaluate their methods on various D4RL benchmark tasks, including locomotion, manipulation, and robotic control scenarios, comparing their performance against established offline RL algorithms.
Key Findings:
- RCDTP and RWDTP demonstrate comparable, and in some cases, superior performance to state-of-the-art offline RL methods, particularly on medium-expert and expert datasets.
- Both frameworks exhibit significantly faster training and inference times compared to deep learning-based approaches, often completing training within minutes on a CPU.
- The use of decision trees provides inherent explainability, allowing for analysis of feature importance and action distributions.
- RCDTP and RWDTP show robustness in delayed/sparse reward scenarios and exhibit promising zero-shot transfer capabilities in robotic control tasks.
Main Conclusions:
The study demonstrates the effectiveness of reframing offline RL as a regression problem solvable by decision trees. RCDTP and RWDTP offer a compelling alternative to traditional deep learning methods, providing a computationally efficient, explainable, and high-performing approach for offline policy learning.
Significance:
This research contributes to the field of offline RL by introducing novel, computationally efficient, and explainable policy learning methods. The use of decision trees opens up new possibilities for real-time control and robot learning applications where fast inference and interpretability are crucial.
Limitations and Future Research:
- The current work primarily focuses on flat-structured observation spaces, limiting its applicability to complex data modalities like images and text.
- The offline training paradigm restricts the models' ability to adapt to new experiences or environments.
- Future research could explore extensions for handling multimodal datasets and incorporating online learning capabilities.
Stats
RWDTP and RCDTP training times are less than 1% of the GPU training time for Decision Transformer (DT) and Trajectory Transformer (TT) on Hopper expert datasets.
RCDTP and RWDTP achieve expert-level returns in Cartpole and Pendulum environments within a second of training.
RWDTP and RCDTP outperform Decision Transformer in a zero-shot transferability test on the F1tenth racing scenario.
Quotes
"By employing an ‘extreme’ gradient boosting algorithm for regression over actions space, we train the agent’s policy as an ensemble of weak policies, achieving rapid training times of mere minutes, if not seconds."
"Besides replacing neural networks as the default function approximators in RL, our paper also introduces two offline RL frameworks: Return Conditioned Decision Tree Policy (RCDTP) and Return Weighted Decision Tree Policy (RWDTP)."
"These methods embody simplified modeling strategies, expedited training methodologies, faster inference mechanisms, and require minimal hyperparameter tuning, while also offering explainable policies."