Learning Markov State Abstractions for Deep Reinforcement Learning

Core Concepts

Learning a Markov abstract state representation is crucial for improving deep reinforcement learning efficiency.

Abstract

The article introduces a novel approach to learning Markov state abstractions in the context of deep reinforcement learning. It addresses the challenge of preserving the Markov property in abstract state representations, which are essential for effective decision-making in complex environments. By combining inverse model estimation and temporal contrastive learning, the proposed method aims to learn representations that capture the underlying structure of domains and enhance sample efficiency. The study evaluates the approach on visual gridworld navigation tasks and continuous control benchmarks, demonstrating improved performance over existing methods. The training objective focuses on ensuring that learned abstractions are Markov while avoiding representation collapse without relying on reward information or ground-state prediction.

Stats

Code repository available at https://github.com/camall3n/markov-state-abstractions. 35th Conference on Neural Information Processing Systems (NeurIPS 2021). arXiv:2106.04379v4 [cs.LG] 15 Mar 2024

Quotes

"We introduce a new approach to learning Markov state abstractions." "Our approach learns abstract state representations that capture the underlying structure of the domain." "Our method is effective for learning Markov state abstractions that are highly beneficial for decision making."

Key Insights Distilled From

Learning Markov State Abstractions for Deep Reinforcement Learning

by Cameron Alle... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2106.04379.pdf

Deeper Inquiries

How can this approach be extended to more complex environments beyond visual gridworlds

To extend this approach to more complex environments beyond visual gridworlds, we can adapt the training procedure and conditions for learning a Markov abstraction. In more complex environments with higher-dimensional state spaces, we may need to use more advanced neural network architectures to handle the increased complexity of observations. Additionally, incorporating techniques such as hierarchical abstractions or multi-level representations could help capture the underlying structure of the environment in a more efficient manner. By adjusting the training objectives and loss functions to suit the specific characteristics of different domains, we can apply this method effectively in a wide range of settings.

What potential limitations or drawbacks might arise from relying solely on inverse model estimation and contrastive learning

Relying solely on inverse model estimation and contrastive learning may have some limitations and drawbacks. One potential limitation is that these methods may struggle in highly stochastic environments where predicting next states accurately is challenging. Additionally, without considering other factors such as reward information or exploration strategies, there is a risk of overfitting to certain patterns in the data or missing important aspects of the environment dynamics. Furthermore, these approaches may not be suitable for all types of tasks or domains, especially those with sparse rewards or complex interactions between states.

How could incorporating exploration strategies impact the effectiveness of this method in sparse reward settings

Incorporating exploration strategies can significantly impact the effectiveness of this method in sparse reward settings by ensuring that the agent explores diverse parts of the state space and gathers informative experiences for learning an effective abstraction. Exploration helps prevent representation collapse by exposing the agent to various scenarios and transitions that might not occur frequently under a deterministic policy. By combining exploration strategies with inverse model estimation and contrastive learning, agents can learn robust representations that capture essential features of their environment even when rewards are scarce or non-existent.