Summary of A Single Goal Is All You Need: Skills and Exploration Emerge From Contrastive Rl Without Rewards, Demonstrations, or Subgoals, by Grace Liu et al.
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals
by Grace Liu, Michael Tang, Benjamin Eysenbach
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents empirical evidence of skills emerging from a simple reinforcement learning (RL) algorithm before successful trials are observed. For example, in a manipulation task, the agent learns skills for moving its end-effector, pushing a block, and picking up and placing the block without reward functions, demonstrations, or manually-specified distance metrics. The agent learns to reach the goal state reliably, and exploration is reduced once this skill is acquired. The proposed method involves a simple modification of prior work and does not require density estimates, ensembles, or additional hyperparameters. The authors lack a clear theoretical understanding of why it works effectively, but experiments provide some hints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that a simple learning algorithm can figure out how to do things before it actually succeeds at doing those things. For example, an artificial arm learns to move and pick up blocks without being shown what to do or getting rewarded for trying. Once the arm is good at moving the block to where it needs to be, it stops exploring new ways to move the block. The researchers don’t fully understand why this algorithm works so well, but their experiments give some clues. |
Keywords
* Artificial intelligence * Reinforcement learning
