Improving Zero-Shot Generalization in Reinforcement Learning by Learning Behavior-Specific Context Representations

Core Concepts

Jointly learning context representations and policy enables improved zero-shot generalization in reinforcement learning.

Abstract

This paper proposes a novel reinforcement learning (RL) algorithm that jointly learns context representations and policy to improve zero-shot generalization. The key insights are:

1. Inferring context from past experiences is more effective when the context representation is tailored to the specific policy being learned, rather than the full transition dynamics.
2. By backpropagating the policy loss through the context encoder, the learned context embeddings capture information relevant to the current policy, enabling better adaptation to unseen environments.
3. Experiments across multiple simulated environments show that the proposed joint learning approach outperforms prior context-learning techniques in zero-shot generalization settings, particularly in more complex environments like Ant.
4. The learned context embeddings are shown to better capture the underlying changes in the environment dynamics compared to a decoupled context learning approach.

Overall, this work represents a significant step towards creating more autonomous and versatile RL agents that can effectively adapt to diverse real-world tasks without additional training.

Stats

The paper reports the following key metrics:

Episodic return (area under the curve) during training across different environments
Interquartile mean (IQM) of normalized scores in interpolation and extrapolation settings for zero-shot generalization evaluation
Mean squared error of predicting the ground-truth context from the learned context embeddings

Quotes

"By jointly optimizing the context and policy representations, jcpl can discover latent embeddings that encode relevant information about the varying transition dynamics, facilitating improved generalization."
"Our approach of jointly learning behavior-specific context embeddings directly addresses this open question and demonstrates improved generalization performance, especially in complex environments like Ant."

Key Insights Distilled From

Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning

by Tidi... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09521.pdf

Deeper Inquiries

How can the proposed joint learning approach be extended to handle variations in the reward structure, in addition to changes in the environment dynamics

The proposed joint learning approach can be extended to handle variations in the reward structure by incorporating reward signals into the context modeling process. This can be achieved by capturing task-specific information within the learned embeddings, enabling adaptation across diverse tasks with different reward structures. By observing reward signals during evaluation, the context encoder can further enhance context identification and generalization performance. This extension would allow the agent to adapt not only to changes in the environment dynamics but also to variations in the reward structure, making it more versatile and capable of handling a wider range of tasks.

Can the context encoder architecture be further improved to capture the evolution of transitions over time, potentially enhancing the agent's uncertainty assessment and adaptability

The context encoder architecture can be further improved to capture the evolution of transitions over time by implementing more advanced architectures that consider the temporal aspect of transitions. For example, recurrent neural networks (RNNs) or transformers can be used to capture the sequential nature of transitions and how they evolve over time. By incorporating memory and attention mechanisms, the context encoder can better model the dynamics of the environment and how they change over successive transitions. This enhancement would enable the agent to have a more comprehensive understanding of the environment dynamics, leading to improved adaptability and performance across diverse environments.

What are the potential applications and real-world implications of this work on developing more autonomous and versatile reinforcement learning systems

The potential applications and real-world implications of this work on developing more autonomous and versatile reinforcement learning systems are significant. By enabling agents to learn behavior-specific context representations and adapt to unseen environments without additional training, the proposed approach opens up possibilities for various applications.
One key application is in robotics, where autonomous agents need to operate in dynamic and diverse environments. By learning context-aware policies that can generalize across different tasks and environments, robots can perform a wide range of tasks efficiently and effectively without the need for extensive retraining. This can lead to more autonomous and versatile robotic systems that can adapt to changing conditions and tasks in real-time.
Furthermore, in fields like autonomous driving, healthcare, and manufacturing, where tasks and environments can vary significantly, context-aware reinforcement learning systems can provide adaptive and intelligent solutions. These systems can learn to infer context from past experiences, enabling them to make informed decisions and adapt their behavior based on the specific requirements of the task at hand. Overall, the work has the potential to revolutionize various industries by creating more adaptable, versatile, and autonomous systems that can operate effectively in complex and dynamic environments.