[Paper Review] On the role of planning in model-based deep reinforcement learning

Jessica B. Hamrick, Abram L. Friesen+8 more|arXiv (Cornell University)|May 3, 2021

Reinforcement Learning in Robotics68 references26 citations

TL;DR

This paper investigates the role of planning in model-based deep reinforcement learning (MBRL) by analyzing MuZero across diverse environments. It finds that planning significantly enhances learning efficiency and data quality, shallow trees with Monte-Carlo rollouts are highly effective, and planning alone is insufficient for strong generalization.

ABSTRACT

Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve generalization? To answer these questions, we study the performance of MuZero (Schrittwieser et al., 2019), a state-of-the-art MBRL algorithm with strong connections and overlapping components with many other MBRL algorithms. We perform a number of interventions and ablations of MuZero across a wide range of environments, including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) Planning is most useful in the learning process, both for policy updates and for providing a more useful data distribution. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.

Motivation & Objective

• To understand how planning contributes to the success of model-based deep reinforcement learning (MBRL) agents.
• To identify which components of planning—such as tree depth or rollout strategy—most influence performance.
• To evaluate whether planning enables strong generalization across unseen environments.
• To disentangle the contributions of planning from other components in state-of-the-art MBRL algorithms like MuZero.

Proposed method

• The authors conduct systematic ablations and interventions on MuZero, a state-of-the-art MBRL algorithm with broad architectural and functional overlap with other MBRL methods.
• They modify planning components, including tree depth, rollout strategy (e.g., Monte-Carlo vs. value-guided), and planning frequency, across control tasks, Atari games, and 9x9 Go.
• Performance is evaluated across learning efficiency, final agent performance, and generalization to unseen environments.
• The study uses a unified framework to isolate the impact of planning from model learning, policy optimization, and exploration.
• Interventions include disabling planning entirely, replacing complex planning with shallow trees, and using random rollouts instead of learned value estimates.
• Experiments are conducted on a diverse benchmark including continuous control, discrete control, and board games to assess robustness across domains.

Experimental results

Research questions

• RQ1 How does planning specifically benefit the training process in model-based deep reinforcement learning?
• RQ2 Which aspects of the planning mechanism—such as tree depth or rollout strategy—most significantly affect performance?
• RQ3 To what extent does planning improve generalization to out-of-distribution environments?
• RQ4 Can simpler planning mechanisms match the performance of more complex ones, and under what conditions?

Key findings

• Planning is most beneficial during the learning phase, improving policy updates and shaping a more useful data distribution for training.
• Shallow trees with simple Monte-Carlo rollouts achieve performance comparable to complex planning methods, except in the most challenging reasoning tasks.
• Planning alone is insufficient to drive strong generalization, indicating that other components such as world model capacity or exploration are critical.
• The performance gains from planning are most pronounced in early training, suggesting it acts as a strong credit assignment and data augmentation mechanism.
• In environments requiring deep reasoning, such as 9x9 Go, more sophisticated planning strategies provide a measurable advantage over simpler rollouts.
• The results highlight that planning's primary value lies in improving sample efficiency and policy learning, rather than in intrinsic reasoning capacity.