Recall to Imagine (R2I): A New Machine Learning Approach that Enhances Long-Term Memory by Incorporating State Space Models into Model-based Reinforcement Learning (MBRL)

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Recall to Imagine (R2I): A New Machine Learning Approach

Enhancing Long-Term Memory with State Space Models in Model-based Reinforcement Learning (MBRL)

With the recent advancements in Machine Learning (ML) and Reinforcement Learning (RL), Model-Based Reinforcement Learning (MBRL) has gained popularity. MBRL agents learn world models from previous experiences to forecast action results and make informed decisions.

One major challenge in MBRL is managing long-term dependencies, where agents struggle with distant observations and temporal gaps. To address this, the ‘Recall to Imagine’ (R2I) method incorporates state space models (SSMs) to enhance long-term memory and credit assignment.

R2I has proven its effectiveness through extensive evaluations across various tasks, setting new performance benchmarks in demanding RL environments and demonstrating superhuman performance in memory-intensive tasks like the Memory Maze challenge.

R2I not only excels in memory-intensive tasks but also performs comparably in standard RL tasks, showcasing its generalizability and efficiency. It converges more quickly than the most advanced MBRL approach, making it suitable for time-critical real-world applications.

The primary contributions of R2I include improved memory capacity, faster convergence, and superior performance in memory-intensive domains compared to its competitors.

For more details, refer to the paper.

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Recall to Imagine (R2I): A New Machine Learning Approach that Enhances Long-Term Memory by Incorporating State Space Models into Model-based Reinforcement Learning (MBRL)

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