Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals

Export BriefConnect with Author
View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (65)

[1]
The impact of intrinsic rewards on exploration in Reinforcement Learning
2025Aya Kayal, Eduardo Pignatelli et al.
[2]
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
2023Alexander Nikulin, Vladislav Kurenkov et al.
[3]
Human-Timescale Adaptation in an Open-Ended Task Space
2023Adaptive Agent Team, Jakob Bauer et al.
[4]
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
2022Sai Rajeswar, Pietro Mazzaglia et al.
[5]
Reinforcement Learning with Action-Free Pre-Training from Videos
2022Younggyo Seo, Kimin Lee et al.
[6]
Unsupervised Reinforcement Learning in Multiple Environments
2021Mirco Mutti, Mattia Mancassola et al.
[7]
Masked Autoencoders Are Scalable Vision Learners
2021Kaiming He, Xinlei Chen et al.
[8]
Learning a subspace of policies for online adaptation in Reinforcement Learning
2021Jean-Baptiste Gaya, L. Soulier et al.
[9]
Pretraining Representations for Data-Efficient Reinforcement Learning
2021Max Schwarzer, Nitarshan Rajkumar et al.
[10]
Behavior From the Void: Unsupervised Active Pre-Training
2021Hao Liu, P. Abbeel
[11]
Asymmetric self-play for automatic goal discovery in robotic manipulation
2021OpenAI OpenAI, Matthias Plappert et al.
[12]
Open-Ended Learning Leads to Generally Capable Agents
2021Open-Ended Learning Team, Adam Stooke et al.
[13]
Prioritized Level Replay
2020Minqi Jiang, Edward Grefenstette et al.
[14]
Decoupling Representation Learning from Reinforcement Learning
2020Adam Stooke, Kimin Lee et al.
[15]
GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning
2020Grgur Kovač, A. Laversanne-Finot et al.
[16]
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
2020Andres Campero, R. Raileanu et al.
[17]
Learning Invariant Representations for Reinforcement Learning without Reconstruction
2020Amy Zhang, R. McAllister et al.
[18]
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
2020Jean-Bastien Grill, Florian Strub et al.
[19]
Language Models are Few-Shot Learners
2020Tom B. Brown, Benjamin Mann et al.
[20]
Planning to Explore via Self-Supervised World Models
2020Ramanan Sekar, Oleh Rybkin et al.

Showing 20 of 65 references

Founder's Pitch

"Developing adaptive RL policies through unsupervised goal-setting for diverse environments."

Reinforcement LearningScore: 4View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

1/4 signals

2.5

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 1/27/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.

Related Papers

Loading…

Related Resources