UniZero: Generalized and Efficient Planning with Scalable Latent World Models (2406.10667v1)

Abstract: Learning predictive world models is essential for enhancing the planning capabilities of reinforcement learning agents. Notably, the MuZero-style algorithms, based on the value equivalence principle and Monte Carlo Tree Search (MCTS), have achieved superhuman performance in various domains. However, in environments that require capturing long-term dependencies, MuZero's performance deteriorates rapidly. We identify that this is partially due to the \textit{entanglement} of latent representations with historical information, which results in incompatibility with the auxiliary self-supervised state regularization. To overcome this limitation, we present \textit{UniZero}, a novel approach that \textit{disentangles} latent states from implicit latent history using a transformer-based latent world model. By concurrently predicting latent dynamics and decision-oriented quantities conditioned on the learned latent history, UniZero enables joint optimization of the long-horizon world model and policy, facilitating broader and more efficient planning in latent space. We demonstrate that UniZero, even with single-frame inputs, matches or surpasses the performance of MuZero-style algorithms on the Atari 100k benchmark. Furthermore, it significantly outperforms prior baselines in benchmarks that require long-term memory. Lastly, we validate the effectiveness and scalability of our design choices through extensive ablation studies, visual analyses, and multi-task learning results. The code is available at \textcolor{magenta}{https://github.com/opendilab/LightZero}.

• The paper proposes UniZero, which disentangles latent states from historical data to handle long-term dependencies more effectively.
• UniZero employs a transformer-based latent model and joint optimization that outperforms MuZero on benchmarks like Atari 100k.
• The approach integrates efficient MCTS-based planning with backward memory, demonstrating significant performance gains in challenging RL environments.

Overview of UniZero: Generalized and Efficient Planning with Scalable Latent World Models

The paper "UniZero: Generalized and Efficient Planning with Scalable Latent World Models" introduces a novel approach to enhancing planning capabilities in reinforcement learning (RL) agents. The authors identify limitations in MuZero-style algorithms, particularly concerning scenarios that require accommodating long-term dependencies. To address these challenges, the paper proposes UniZero, a methodology employing a transformer-based latent world model.

Key Contributions

1. Identification of Limitations in MuZero: The research highlights two key issues in MuZero-style architectures. Firstly, there is an entanglement of latent representations with historical information, which complicates the separation of current state features from past interactions. Secondly, such architectures fail to fully utilize trajectory data during training, inhibiting optimal performance in environments necessitating long-term dependencies.
2. Introduction of UniZero: UniZero leverages a transformer-based latent model to disentangle latent states from historical information. This disentanglement allows the model to utilize the entire trajectory during training, providing more effective state regularization and mitigating issues identified in previous works.
3. Empirical Validation: The effectiveness of UniZero is confirmed through rigorous experimentation. In the Atari 100k benchmark, UniZero, even when using single-frame inputs, performs comparably or superiorly to MuZero-style architectures using four consecutive frames. Moreover, in benchmarks requiring long-term memory, UniZero significantly outpaces existing baselines.

Methods and Results

• Transformer-Based Model: The authors employ a transformer backbone, enhancing the model's ability to address environments with long-term memory requirements, such as those encountered in partially observable Markov decision processes (POMDPs).
• Joint Optimization: UniZero simultaneously optimizes the world model and policy, contrasting with the two-stage learning paradigm found in many other frameworks. This unified approach helps maintain consistency between model and policy learning.
• MCTS Integration: By augmenting backward memory capabilities with efficient MCTS-based forward planning, UniZero demonstrates enhanced scalability and efficiency across several benchmarks.

The paper validates UniZero’s effectiveness through intensive experimentation. The model demonstrates an impressive capability to handle both short- and long-term dependencies, outperforming baselines in 17 out of 26 games in the Atari 100k benchmark.

Implications and Future Directions

UniZero represents a significant stride towards more generalized and adaptable RL systems. Its ability to effectively model both short- and long-term dependencies points to its potential as a foundational framework for decision-making tasks that require sophisticated planning capabilities.

Future work could explore domain-specific optimization techniques to further refine the attention mechanisms within the transformer architecture. Moreover, extending the research into multi-task learning setups could unlock even broader applications, making UniZero a versatile tool in the arsenal of AI researchers. Additionally, integrating innovations like optimized attention mechanisms might enhance UniZero's applicability in even more complex environments.

In conclusion, UniZero effectively addresses key limitations in prior RL architectures, presenting robust solutions that enhance both theoretical understanding and practical application of latent world models in AI research.