# Gotta Learn Fast: A New Benchmark for Generalization in RL

arXiv: https://arxiv.org/abs/1804.03720 · HTML: https://mlsanity.com/paper/1804.03720/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 30 of 59)

- [AdaWorld: Learning Adaptable World Models with Latent Actions](https://mlsanity.com/paper/2503.18938/) (2025)
- [Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability](https://mlsanity.com/paper/2107.06277/) (2021)
- [MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research](https://mlsanity.com/paper/2109.13202/) (2021)
- [Decoupling Value and Policy for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2102.10330/) (2021)
- [Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning](https://mlsanity.com/paper/2107.00848/) (2021)
- [Atari-5: Distilling the Arcade Learning Environment down to Five Games](https://mlsanity.com/paper/2210.02019/) (2022)
- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents](https://mlsanity.com/paper/2102.02926/) (2021)
- [Procedural Generalization by Planning with Self-Supervised World Models](https://mlsanity.com/paper/2111.01587/) (2021)
- [On the Importance of Exploration for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2306.05483/) (2023)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2106.13948/) (2021)
- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://mlsanity.com/paper/2101.08152/) (2021)
- [How to Stay Curious while Avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://mlsanity.com/paper/2102.04399/) (2021)
- [The Generalization Gap in Offline Reinforcement Learning](https://mlsanity.com/paper/2312.05742/) (2023)
- [Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark](https://mlsanity.com/paper/2103.15332/) (2021)
- [When Is Generalizable Reinforcement Learning Tractable?](https://mlsanity.com/paper/2101.00300/) (2021)
- [TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL](https://mlsanity.com/paper/2103.09815/) (2021)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [Horizon Generalization in Reinforcement Learning](https://mlsanity.com/paper/2501.02709/) (2025)
- [Towards Understanding How Machines Can Learn Causal Overhypotheses](https://mlsanity.com/paper/2206.08353/) (2022)
- [FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.02081/) (2024)
- [HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning](https://mlsanity.com/paper/2406.03997/) (2024)
- [The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design](https://mlsanity.com/paper/2406.17949/) (2024)
- [Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari](https://mlsanity.com/paper/2111.10247/) (2021)
- [Local Feature Swapping for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2204.06355/) (2022)
- [ContraBAR: Contrastive Bayes-Adaptive Deep RL](https://mlsanity.com/paper/2306.02418/) (2023)
- [Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL](https://mlsanity.com/paper/2106.15883/) (2021)
- [On the Generalization and Adaption Performance of Causal Models](https://mlsanity.com/paper/2206.04620/) (2022)
- [Learning Causal Overhypotheses through Exploration in Children and Computational Models](https://mlsanity.com/paper/2202.10430/) (2022)
- [Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation](https://mlsanity.com/paper/2209.10149/) (2022)

## Abstract (arXiv)

In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new benchmark.