Open Source AI Project


This GitHub repository contains the implementation of a novel approach to meta-learn reinforcement learning algorithms.


The GitHub repository introduces a groundbreaking method for enhancing reinforcement learning (RL) through meta-learning. This approach innovates by exploring computation graphs to devise loss functions for value-based, model-free RL agents. These agents are then optimized for performance. The key features of this method include its domain-agnostic nature, allowing the learned algorithms to adapt and perform efficiently in new, unseen environments. This adaptability marks a significant advancement in the field of RL, as it overcomes the limitation of overfitting to specific tasks or scenarios seen during the training phase.

The method stands out by offering the flexibility to either develop new algorithms from scratch or improve upon existing ones, such as the Deep Q-Network (DQN). This adaptability not only fosters the creation of more efficient RL algorithms but also ensures that these enhancements are interpretable, making the modifications understandable and the outcomes predictable.

A notable achievement of this approach is its ability to rediscover temporal difference (TD) algorithms autonomously in simplified settings, such as control and grid world tasks. This demonstrates the method’s foundational understanding of RL principles. Moreover, the emphasis on two learned algorithms that exhibited exceptional generalization capabilities across various tasks—including classic control challenges, grid world tasks, and even complex Atari games—underscores the method’s robustness and its potential to redefine benchmarks in RL performance.

The repository serves as a comprehensive resource, likely providing access to the source code, detailed explanations of the algorithmic innovations, and the datasets used for training and evaluating the algorithms. This makes it an invaluable tool for researchers and practitioners in the field, offering insights into the method’s development and application, facilitating further research, and encouraging the adoption of these advanced RL solutions in diverse applications.

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