Benefit of MCTS-based RL approaches over PPO, SAC, etc for dynamic robot about lightzero

[puyuan1996]

Hello,

• MCTS-based RL approaches, such as AlphaZero/MuZero, may have the following advantages in the field of robotics compared to model-free RL methods like PPO, SAC:

  1. High sample efficiency:
  • Efficient Exploration-Exploitation Trade-off: MCTS employs the Upper Confidence Bound (UCB) algorithm to strike a balance between exploration and exploitation, thereby enhancing the algorithm's effectiveness in identifying the optimal policy. Notably, MCTS doesn't indiscriminately expand all nodes; instead, it selectively expands based on their predicted prior policy logits and predicted value. This approach prioritizes actions that are anticipated to yield high returns, consequently improving sample efficiency.
  • Forward search: MCTS predicts future rewards by starting from the current state and searching forward, which allows it to consider longer-term returns. Compared to methods that only rely on single-step rewards, it can use each sample more effectively.
  • Use of model prediction: In model-based MCTS (like MuZero), the model's prediction is used to guide the search. This means that each sample is fully utilized to improve the model's prediction, thereby improving sample efficiency.

  1. Interpretability: MCTS provides an intuitive and easy-to-visualize approach for the decision-making process, allowing people to better understand and interpret the behavior of the robot. This can be very important in some applications, such as scenarios that require human-robot collaboration.

  2. Better handling of complex and delayed reward functions: MCTS can find long-term rewards through search, even in the case of reward delay. This makes MCTS potentially perform better in tasks that require complex strategies and long-term planning, such as multi-step manipulation tasks, compared to PPO, SAC, etc.

  3. Adaptability: MCTS can conduct a new search at each decision step, allowing it to adapt to dynamic changes in the environment, especially in those dynamic robotic tasks that require real-time decisions and responses.

• However, it should be noted that MCTS usually requires a large amount of computational resources and may be slower than model-free RL methods in the early stage of training. In addition, MCTS may also need carefully designed reward functions and models for effective search.

• You might refer to relevant papers like Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees for more information. If you come across other insightful papers on MCTS used in real robotic environments, feel free to add them to this issue.

• Please note that we currently do not have much practical experience in developing actual robots. The above is only an intuitive analysis and is for reference only.