Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (1911.08265v2)

Abstract: Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

• The paper introduces MuZero, which integrates model-based planning with a learned dynamics model to achieve superhuman performance across diverse games.
• MuZero employs a predictive model to estimate rewards, policies, and value functions, eliminating the need for explicit knowledge of game rules.
• Experimental results reveal MuZero outperforms prior methods on 57 Atari games and matches AlphaZero in board games like Go, chess, and shogi.

Mastering Atari, Go, Chess, and Shogi by Planning with a Learned Model

The paper entitled "Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model" introduces the MuZero algorithm, a novel approach in model-based reinforcement learning (RL) that integrates the benefits of both model-free and model-based approaches. MuZero is distinct for its capacity to achieve superhuman performance across a spectrum of complex domains, without the necessity of prior knowledge about the environment's dynamics, thus representing a sophisticated advancement in the field of artificial intelligence.

MuZero operates by learning a model that iteratively forecasts the reward, policy distribution, and value function necessary for planning. Unlike traditional tree-based planning algorithms that rely on exact environmental models, MuZero learns an implicit model that provides sufficient detail for decision-making and policy optimization. It evaluates 57 Atari games, a standard for visually complex environments, alongside classic board games of Go, chess, and shogi, to demonstrate its effectiveness.

Algorithm Overview

MuZero leverages a tree-based search similar to AlphaZero but innovates by integrating a learned dynamics model. This method departs from the traditional model-based RL dependence on a fully-known state transition model or simulator. Instead, MuZero’s model predicts three aspects critical for planning: immediate rewards, the action-selection policy, and the value function, all derived from the current state and history of observations.

The algorithm’s training pipeline integrates:

1. A representation function converting past observations into a hidden state.
2. A dynamics function transitioning between hidden states based on chosen actions.
3. A prediction function outputting policy and value estimates from hidden states.

The training allows the agent to optimize these components end-to-end using backpropagation through time, training the model to minimize prediction errors in policy, value, and reward, as metrics for the environment's actual behavior.

Results

The remarkable efficacy of MuZero is illustrated in its performance metrics:

1. Atari 2600 Games: MuZero achieved a state-of-the-art performance across 57 games, outperforming methods like R2D2 in 42 of those games. Its average and median human-normalized scores were significantly higher than previous model-free and model-based methods.
2. Board Games: In chess, shogi, and Go, MuZero equaled the capabilities of AlphaZero—which was provided with the rules—despite receiving no information about the games' rules. This showcases MuZero's superior adaptability and robust understanding developed through learning.

Practical and Theoretical Implications

The implications of MuZero extend broadly:

1. Real-World Applications: MuZero's approach is versatile for real-world problems where dynamics are unknown, such as robotics, industrial control systems, and personal assistive technologies. It bypasses the constraint of requiring exhaustive knowledge or perfect simulators, key for practical scalability.
2. Theoretical Insights: On a theoretical level, MuZero contributes to understanding how RL systems can generate efficient policies without direct environmental models, focusing instead on predictive accuracy of essential planning components.

Future Developments

Future research directions opened by MuZero include:

• Enhancing the scalability and efficiency of the dynamics model to handle even more complex environments.
• Extending MuZero’s scope to cover multi-agent and layered decision processes, potentially exploring cooperative or adversarial settings beyond zero-sum games.
• Integrating MuZero's principles with other learning paradigms, such as transfer learning, to bolster performance across varied and evolving tasks.

MuZero represents a significant step forward in artificial intelligence by seamlessly combining the strengths of model-free and model-based approaches to dynamically plan and learn in complex environments devoid of explicit knowledge about the environmental rules. This positions it as an essential milestone in the ongoing evolution and potential applications of intelligent systems.