Summary of Black Box Meta-learning Intrinsic Rewards For Sparse-reward Environments, by Octavio Pappalardo et al.
Black box meta-learning intrinsic rewards for sparse-reward environments
by Octavio Pappalardo, Rodrigo Ramele, Juan Miguel Santos
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper aims to improve deep reinforcement learning by addressing several challenges that hinder its broader application. Current approaches struggle with data efficiency, generalization capability, and ability to learn in sparse-reward environments, which often require human-designed dense rewards. To address these issues, the authors explore meta-learning as a promising approach to optimize components of the learning algorithm to meet desired characteristics. The paper also investigates the use of intrinsic rewards to enhance the exploration capabilities of algorithms. Specifically, it examines how meta-learning can improve the training signal received by RL agents without relying on meta-gradients. The developed algorithms are evaluated on distributions of continuous control tasks with both parametric and non-parametric variations, and with only sparse rewards accessible for the evaluation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make deep learning better. Right now, it’s hard to teach computers to do things because they need too much information or don’t generalize well. They also struggle in situations where they get little reward for doing something good. The authors want to find a way to improve this by using something called meta-learning. This means teaching the computer how to learn better rather than just giving it more data. The goal is to make computers that can learn and explore on their own, without needing humans to tell them what’s right or wrong. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Meta learning » Reinforcement learning
