Recall to Imagine (R2I): a new machine learning approach that improves long-term memory by incorporating state space models into model-based reinforcement learning (MBRL)

With recent advancements in the field of machine learning (ML), reinforcement learning (RL), which is one of its branches, has become significantly popular. In RL, an agent acquires skills to interact with its environment by acting in a way that maximizes the sum of its rewards.

Incorporating global models into real life has become a powerful paradigm in recent years. Agents can observe, simulate and plan within the learned dynamics with the help of world models, which summarize the dynamics of the surrounding environment. Model-based reinforcement learning (MBRL) has been facilitated by this integration, in which an agent learns a world model from previous experiences to predict the results of its actions and make accurate judgments.

One of the main problems in the MBRL field is long-term dependency management. These dependencies describe scenarios in which an agent must remember distant observations in order to make judgments or situations in which there are significant temporal gaps between the agent's actions and outcomes. The inability of current MBRL agents to perform well on tasks that require temporal coherence is a result of their frequent struggles with these environments.

To address these issues, a team of researchers has suggested a unique 'Recall to Imagine' (R2I) method to address this issue and improve agents' ability to manage long-term dependency. R2I incorporates a set of state space models (SSM) into the MBRL agent world models. The objective of this integration is to improve the long-term memory capacity of the agents, as well as their credit allocation capacity.

The team has demonstrated the effectiveness of R2I through a comprehensive evaluation of a wide range of illustrative work. First, R2I has set a new benchmark for performance on demanding RL tasks such as memory and credit allocation found in POPGym and BSuite environments. R2I has also demonstrated superhuman performance on the Memory Maze task, a challenging memory domain, demonstrating its ability to manage challenging memory-related tasks.

Not only has R2I performed comparably on standard reinforcement learning tasks such as those in the Atari and DeepMind Control (DMC) environments, but it also excelled on memory-intensive tasks. This implies that this approach is generalizable to different RL scenarios and effective in specific memory domains.

The team has illustrated the effectiveness of R2I by demonstrating that it converges more quickly in terms of wall time compared to DreamerV3, the most advanced MBRL approach. Due to its rapid convergence, R2I is a viable solution for real-world applications where time efficiency is critical and can achieve desirable results more efficiently.

The team has summarized its main contributions as follows:

1. DreamerV3 is the basis for R2I, an enhanced MBRL agent with improved memory. R2I has used a modified version of S4 to manage temporary dependencies. It preserves the generality of DreamerV3 and delivers up to 9x faster calculations while using fixed world model hyperparameters across domains.

1. POPGym, BSuite, Memory Maze, and other memory-intensive domains have shown that R2I performs better than its competitors. R2I performs better than humans, especially in Memory Maze, which is a difficult 3D environment that tests long-term memory.

1. R2I's performance has been evaluated on RL benchmarks such as DMC and Atari. The results highlighted the adaptability of R2I by showing that its enhanced memory capabilities do not degrade its performance on a variety of control tasks.

1. To evaluate the effects of the design decisions made for R2I, the team conducted ablation tests. This provided information on the efficiency of the system architecture and its individual parts.

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