# Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks

arXiv: https://arxiv.org/abs/2410.23208 · HTML: https://mlsanity.com/paper/2410.23208/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 82 of its 111 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 30 of 82)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [MOPO: Model-based Offline Policy Optimization](https://mlsanity.com/paper/2005.13239/) (2020)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://mlsanity.com/paper/2406.02523/) (2024)

## Built on by (most cited first, 30 of 45)

- [Real-Time Execution of Action Chunking Flow Policies](https://mlsanity.com/paper/2506.07339/) (2025)
- [Training-Time Action Conditioning for Efficient Real-Time Chunking](https://mlsanity.com/paper/2512.05964/) (2025)
- [VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference](https://mlsanity.com/paper/2512.01031/) (2025)
- [Leave No Observation Behind: Real-time Correction for VLA Action Chunks](https://mlsanity.com/paper/2509.23224/) (2025)
- [Evolution Strategies at the Hyperscale](https://mlsanity.com/paper/2511.16652/) (2025)
- [Procedural Environment Generation for Tool-Use Agents](https://mlsanity.com/paper/2506.11045/) (2025)
- [FASTER: Rethinking Real-Time Flow VLAs](https://mlsanity.com/paper/2603.19199/) (2026)
- [MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2506.14990/) (2025)
- [An Optimisation Framework for Unsupervised Environment Design](https://mlsanity.com/paper/2505.20659/) (2025)
- [Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics](https://mlsanity.com/paper/2505.13150/) (2025)
- [MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces](https://mlsanity.com/paper/2502.07709/) (2025)
- [vla-eval: A Unified Evaluation Harness for Vision-Language-Action Models](https://mlsanity.com/paper/2603.13966/) (2026)
- [BuilderBench: The Building Blocks of Intelligent Agents](https://mlsanity.com/paper/2510.06288/) (2025)
- [PuzzleJAX: A Benchmark for Reasoning and Learning](https://mlsanity.com/paper/2508.16821/) (2025)
- [DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors](https://mlsanity.com/paper/2604.25050/) (2026)
- [Understanding Asynchronous Inference Methods for Vision-Language-Action Models](https://mlsanity.com/paper/2605.08168/) (2026)
- [Investigating Memory in Model-Free RL with POPGym Arcade](https://mlsanity.com/paper/2503.01450/) (2025)
- [Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments](https://mlsanity.com/paper/2603.06009/) (2026)
- [Reflex: Real-Time VLA Control through Streaming Inference](https://mlsanity.com/paper/2607.14695/) (2026)
- [Multi-Agent Craftax: Benchmarking Open-Ended Multi-Agent Reinforcement Learning at the Hyperscale](https://mlsanity.com/paper/2511.04904/) (2025)
- [Action ControlNet: A Lightweight Delay-Aware Adapter for Smooth Asynchronous Control in Vision-Language-Action Models](https://mlsanity.com/paper/2606.25985/) (2026)
- [Hierarchical Experimentalist Agents](https://mlsanity.com/paper/2606.29315/) (2026)
- [Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency](https://mlsanity.com/paper/2608.23831/) (2026)
- [Reinforcement Learning for Real-Time Vision-Language-Action Policies](https://mlsanity.com/paper/2609.18207/) (2026)
- [EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2501.15129/) (2025)
- [NiceWebRL: a Python library for human subject experiments with reinforcement learning environments](https://mlsanity.com/paper/2508.15693/) (2025)
- [Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner](https://mlsanity.com/paper/2604.05112/) (2026)
- [BilliardPhys-Bench: Benchmarking Physical Reasoning and Visual Dynamics of Multimodal LLMs](https://mlsanity.com/paper/2605.30900/) (2026)
- [The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks](https://mlsanity.com/paper/2606.28529/) (2026)
- [In-Context Reinforcement Learning under Non-Stationarity: A Survey](https://mlsanity.com/paper/2607.11906/) (2026)

## Abstract (arXiv)

While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for agents that act in sequential decision problems remains an open challenge. In this work, we take a step towards this goal by procedurally generating tens of millions of 2D physics-based tasks and using these to train a general reinforcement learning (RL) agent for physical control. To this end, we introduce Kinetix: an open-ended space of physics-based RL environments that can represent tasks ranging from robotic locomotion and grasping to video games and classic RL environments, all within a unified framework. Kinetix makes use of our novel hardware-accelerated physics engine Jax2D that allows us to cheaply simulate billions of environment steps during training. Our trained agent exhibits strong physical reasoning capabilities in 2D space, being able to zero-shot solve unseen human-designed environments. Furthermore, fine-tuning this general agent on tasks of interest shows significantly stronger performance than training an RL agent *tabula rasa*. This includes solving some environments that standard RL training completely fails at. We believe this demonstrates the feasibility of large scale, mixed-quality pre-training for online RL and we hope that Kinetix will serve as a useful framework to investigate this further.