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MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning (2403.09859v1)

Published 14 Mar 2024 in cs.LG

Abstract: Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to $15\times$) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.

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Citations (4)
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Summary

  • The paper introduces MAMBA as a new model-based meta-RL technique that leverages full meta-episode encoding, local prediction windows, and horizon scheduling.
  • It significantly outperforms existing meta-RL and model-based baselines, achieving up to 15 times greater sample efficiency across benchmarks.
  • The approach demonstrates robust generalization in both low- and high-dimensional tasks, paving the way for adaptable RL agents in complex environments.

MAMBA: A Model-Based Approach to Meta-RL with World Models

Introduction to MAMBA

In the rapidly evolving field of Reinforcement Learning (RL), the concept of Meta-Reinforcement Learning (Meta-RL) has gained significant attention for its potential to generalize and efficiently solve a broad spectrum of tasks. Traditionally, meta-RL algorithms have primarily been based on model-free methods, which, despite their success, often suffer from low sample efficiency and are primarily effective on low-dimensional task distributions. Concurrently, model-based RL methods have demonstrated superior sample efficiency and flexibility by leveraging learned models of the environment. Among these, the Dreamer algorithm has shown promising results in handling partially observable Markov decision processes (POMDPs), which can be considered a general formulation of meta-RL problems.

Building on the success of Dreamer and recognizing its structural similarities with state-of-the-art meta-RL methods, this paper introduces MAMBA (MetA-RL Model-Based Algorithm). MAMBA is a novel approach that combines the strengths of model-based planning with the generalization capabilities required for meta-RL. MAMBA significantly outperforms existing meta-RL and model-based RL baselines across several benchmarks, demonstrating its efficacy and sample efficiency.

Background and Problem Formulation

Meta-RL poses the challenge of learning a policy that can quickly adapt to new tasks sampled from a distribution of related tasks. This capability is crucial for deploying RL agents in real-world scenarios where they must exhibit broad, adaptive behavior. One approach to meta-RL, context-based meta-RL, relies on encoding trajectories into latent variables that represent the task context or belief. However, these methods often encounter challenges related to sample efficiency.

Model-based RL, particularly algorithms like Dreamer, have shown promise in addressing these challenges by learning a model of the environment's dynamics and using it to generate synthetic data for policy training. Dreamer's approach, which uses a recurrent state space model (RSSM) for creating latent representations of trajectories, is particularly adept at handling long-term dependencies and partial observability, making it an attractive foundation for tackling meta-RL tasks.

Technical Approach of MAMBA

MAMBA adapts Dreamer to meta-RL settings through several key modifications:

  1. Full Meta-Episode Encoding: To retain task-relevant information throughout the meta-episode, MAMBA encodes the entire trajectory, from start to finish, into latent representations. This adjustment ensures the inclusion of all available task identifiers within the meta-episode.
  2. Local World Model Prediction Window: MAMBA utilizes a local prediction window for the world model, aligning with the algorithm's focus on capturing and utilizing immediate, relevant task information within the context of meta-RL sub-tasks.
  3. World Model Horizon Scheduling: To address the potential inaccuracies of long-term predictions, especially in the initial stages of training, MAMBA employs a scheduling mechanism that progressively increases the prediction horizon of the world model as training progresses.

Empirical Evaluation and Implications

Empirical evaluation on benchmark domains, including both low-dimensional tasks and novel, challenging high-dimensional domains, demonstrates MAMBA's superior performance in terms of return and sample efficiency. Key findings indicate:

  • Generalization across Meta-RL Benchmarks: MAMBA consistently achieves higher returns compared to both meta-RL and model-based baselines, showcasing its robust generalization capability.
  • Sample Efficiency: MAMBA demonstrates up to 15 times improvement in sample efficiency over state-of-the-art meta-RL algorithms, highlighting the benefits of its model-based approach.
  • Flexibility with High-Dimensional Task Distributions: Through a theoretical analysis and empirical validation, MAMBA proves effective in solving decomposable meta-RL environments, a significant step towards tackling real-world, complex tasks.

Conclusion and Future Directions

MAMBA represents a significant advancement in meta-RL, offering a sample-efficient, generalizable, and robust approach to learning policies across a wide range of tasks. By leveraging the strengths of model-based planning within a meta-RL framework, MAMBA opens new avenues for research and application in domains requiring fast adaptation and broad generalization capabilities. Future work might explore further optimizations to MAMBA's runtime and expand its applicability to more varied and complex task distributions, paving the way towards deploying RL agents in dynamic, real-world environments.

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