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About this paper
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver
DeepMind researchers extended model-based planning algorithms, notably MuZero, to operate effectively in continuous and high-dimensional action spaces. Their method introduces a theoretical framework for sample-based policy iteration using importance sampling corrections, achieving performance comparable to full MuZero on discrete tasks with significantly fewer actions and outperforming model-free baselines on continuous control problems with as few as 3-10 samples.
Key insights
- A principled importance sampling correction can maintain theoretical guarantees for policy iteration algorithms even when actions are sampled rather than exhaustively enumerated.
- MuZero's MCTS search process, when modified with importance sampling for action selection, can effectively operate in complex action spaces.
- For high-dimensional continuous control, surprisingly few action samples (e.g., 3-10) are sufficient to achieve strong performance, making the approach computationally practical.
Summary by alphaXiv.
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