Towards mental time travel: a hierarchical memory for reinforcement learning agents (2105.14039v3)

Abstract: Reinforcement learning agents often forget details of the past, especially after delays or distractor tasks. Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks. To address these limitations, we propose a Hierarchical Chunk Attention Memory (HCAM), which helps agents to remember the past in detail. HCAM stores memories by dividing the past into chunks, and recalls by first performing high-level attention over coarse summaries of the chunks, and then performing detailed attention within only the most relevant chunks. An agent with HCAM can therefore "mentally time-travel" -- remember past events in detail without attending to all intervening events. We show that agents with HCAM substantially outperform agents with other memory architectures at tasks requiring long-term recall, retention, or reasoning over memory. These include recalling where an object is hidden in a 3D environment, rapidly learning to navigate efficiently in a new neighborhood, and rapidly learning and retaining new object names. Agents with HCAM can extrapolate to task sequences much longer than they were trained on, and can even generalize zero-shot from a meta-learning setting to maintaining knowledge across episodes. HCAM improves agent sample efficiency, generalization, and generality (by solving tasks that previously required specialized architectures). Our work is a step towards agents that can learn, interact, and adapt in complex and temporally-extended environments.

• The paper introduces HCAM, a hierarchical memory that segments experiences into chunks, enabling detailed recall over extended periods.
• It employs a two-level attention mechanism—first summarizing and then refining recall—to handle tasks such as ballet sequences, object permanence, and rapid word learning.
• HCAM demonstrates superior extrapolation and cross-episodic memory retention, outperforming conventional memory architectures in diverse RL tasks.

Analysis of "Towards Mental Time Travel: A Hierarchical Memory for Reinforcement Learning Agents"

The paper "Towards Mental Time Travel: A Hierarchical Memory for Reinforcement Learning Agents" introduces a novel memory architecture termed Hierarchical Chunk Attention Memory (HCAM). This memory paradigm aims to enhance reinforcement learning (RL) agents by allowing them to perform detailed recall over extended temporal horizons, thereby improving their ability to manage tasks that require long-term memory, extrapolation beyond their training distribution, and retention of information across distractions or delays.

Memory Architecture

HCAM is presented as a solution to the limitations of existing memory architectures, such as LSTMs or Transformers, in RL settings where long-term memory is crucial. The essence of HCAM lies in its hierarchical structure, which segments past experiences into coherent "chunks", allowing for the sparing yet efficient recall of extensive event sequences. This is achieved by performing a coarse-level attention over summaries of these memory chunks, followed by a detailed attention mechanism that targets the most relevant chunks. This approach is designed to mimic the hierarchical nature of human memory, often referred to as "mental time travel."

Experimental Evaluation

The paper evaluates HCAM across diverse experimental settings that simulate various cognitive demands encountered in real-world tasks. These include:

1. Ballet Task: This experiment places an RL agent in a 2D environment where it must recall the sequence of events (dances) performed by surrounding objects. HCAM demonstrated superior performance in recalling detailed sequences compared to baseline architectures.
2. Object Permanence and Rapid Word Learning: Here, HCAM's efficacy was tested in a 3D environment, tackling tasks inspired by cognitive psychology, such as object permanence with varying temporal delays, and rapid word learning with distraction phases. HCAM managed to maintain and recall learned words even after multiple episodes, outperforming other architectures.
3. Extrapolation and Generalization: HCAM showed robust generalization capabilities, performing well on tasks with complexities an order of magnitude greater than those in its training set. Notably, HCAM was able to retrieve words learned several episodes earlier, indicating strong memory retention and generalization beyond typical episode-based meta-learning.
4. Cross-Episodic Memory: Significantly, HCAM outperformed counterparts in tasks where memory integration across episodes was necessary, simulating a form of continual learning where previous learnings are retained without interference.

Implications and Future Prospects

HCAM presents practical implications for developing RL agents capable of operating in dynamic and temporally-complex environments reminiscent of real-world conditions. The hierarchical memory structure holds promise for future developments in AI, particularly in continual learning and autonomous cognitive agents, where memory recall and integration of past experiences are vital.

The introduction of HCAM opens avenues for further research into optimizing chunking strategies and expanding hierarchical layers to manage longer temporal sequences more efficiently. Moreover, exploring the synergy between HCAM and other state-of-the-art architectures may yield insights into building even more effective agent memories.

In conclusion, the paper positions HCAM as a significant step towards developing RL agents with sophisticated memory capabilities, paralleling aspects of human episodic memory in computational efficiency and recall specificity. This advance could potentially bridge the gap between isolated task learning and comprehensive, integrated task management, fostering future AI systems' ability to adapt and thrive in varied and unpredictable environments.