Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning at ICLR 2024

Core Concepts

Skipper, a model-based reinforcement learning framework, utilizes spatio-temporal abstractions inspired by human conscious planning to improve generalization in novel situations.

Abstract

The article introduces Skipper, a model-based reinforcement learning framework that decomposes tasks into subtasks using abstracted proxy problems. It focuses on spatial and temporal abstraction to enhance generalization. The approach involves generating checkpoints based on partial state information and estimating connections between them. The paper discusses the design choices, edge estimation techniques, and vertex generation methods employed by Skipper. Experimental results demonstrate Skipper's superior generalization performance compared to state-of-the-art hierarchical planning methods like LEAP and Director.

Stats

Published as a conference paper at ICLR 2024
Training tasks difficulty set at 0.4 probability of lava-filled cells
Evaluation tasks sampled with difficulties of 0.25, 0.35, 0.45, and 0.55
Agents trained for 1.5 million interactions

Quotes

"Building on previous work on spatial abstraction (Zhao et al., 2021), we proposed, analyzed and validated Skipper."
"A major unsatisfactory aspect of this work is that we generated checkpoints at random by sampling the partial description space."

Key Insights Distilled From

Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning

by Mingde Zhao,... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2310.00229.pdf

Deeper Inquiries

How does the delusion suppression technique impact the accuracy of proxy problems in Skipper?

In Skipper, the delusion suppression technique plays a crucial role in improving the accuracy of proxy problems by minimizing the behavior of chasing non-existent outcomes. This technique exposes edge estimation to imagined targets that do not exist in the experience replay buffer. By doing so, it helps prevent the agent from forming plans based on unrealistic or delusional targets, which could lead to inaccurate estimations and suboptimal decision-making.
The delusion suppression technique ensures that edge estimation is grounded in realistic scenarios and relevant information from the environment. It helps maintain focus on achievable goals and prevents the agent from getting sidetracked by imaginary or unattainable objectives. Ultimately, this leads to more accurate estimations of checkpoint transitions and better quality proxy problems for planning.

How can relying on memorization for training tasks impact reinforcement learning?

Relying on memorization for training tasks in reinforcement learning can have several negative implications:

Limited Generalization: Memorizing specific solutions to individual tasks without understanding underlying principles limits an agent's ability to generalize its learnings to new situations. The agent may struggle when faced with novel environments or tasks outside its training set.

Overfitting: Over-reliance on memorization can lead to overfitting, where the agent performs well on known tasks but fails when presented with variations or unseen scenarios due to lack of adaptability.

Lack of Robustness: A model that relies heavily on memorized patterns may be fragile and prone to errors when faced with changes in input data or environmental conditions.

High Computational Cost: Memorizing large amounts of data requires significant computational resources and memory capacity, making it inefficient compared to models that learn generalizable representations through understanding underlying concepts.

Overall, while memorization may provide short-term performance gains on specific tasks during training, it hinders long-term success by limiting adaptability and generalization capabilities essential for real-world applications.

How can spatial abstraction be further enhanced beyond using a local perception field selector like Skipper?

To enhance spatial abstraction beyond utilizing a local perception field selector as seen in Skipper, several strategies can be considered:

Dynamic Attention Mechanisms: Implement dynamic attention mechanisms that adjust focus based on task requirements or context changes within an environment.

Hierarchical Abstraction Levels: Introduce multiple levels of spatial abstraction hierarchy where higher levels capture broader environmental features while lower levels focus on finer details.

Contextual Information Integration: Incorporate contextual information into spatial abstractions to enable adaptive decision-making based on situational cues.

Multi-Modal Representations: Utilize multi-modal representations combining different types of sensory inputs (e.g., visual, auditory) for richer spatial abstractions.

5Graph Neural Networks (GNNs): Employ GNNs for capturing complex relationships between entities within an environment leading to more sophisticated spatial abstractions.
By incorporating these advanced techniques into spatial abstraction frameworks like Skipper's local perception field selector approach, agents can develop more nuanced understandings of their surroundings leading to improved decision-making abilities across diverse environments and tasks.