# Towards General-Purpose Model-Free Reinforcement Learning

arXiv: https://arxiv.org/abs/2501.16142 · HTML: https://mlsanity.com/paper/2501.16142/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 78 of its 118 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 30 of 78)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Provably Efficient Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/1907.05388/) (2019)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [Learning Invariant Representations for Reinforcement Learning without Reconstruction](https://mlsanity.com/paper/2006.10742/) (2020)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning](https://mlsanity.com/paper/2107.09645/) (2021)

## Built on by (most cited first, 30 of 41)

- [FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control](https://mlsanity.com/paper/2505.22642/) (2025)
- [Hyperspherical Normalization for Scalable Deep Reinforcement Learning](https://mlsanity.com/paper/2502.15280/) (2025)
- [TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2510.00739/) (2025)
- [A Survey of State Representation Learning for Deep Reinforcement Learning](https://mlsanity.com/paper/2506.17518/) (2025)
- [XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning](https://mlsanity.com/paper/2509.25174/) (2025)
- [Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents](https://mlsanity.com/paper/2510.13704/) (2025)
- [Relative Entropy Pathwise Policy Optimization](https://mlsanity.com/paper/2507.11019/) (2025)
- [The Surprising Difficulty of Search in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2601.21306/) (2026)
- [SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning](https://mlsanity.com/paper/2508.12394/) (2025)
- [Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization](https://mlsanity.com/paper/2508.20294/) (2025)
- [Stable Deep Reinforcement Learning via Isotropic Gaussian Representations](https://mlsanity.com/paper/2602.19373/) (2026)
- [FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control](https://mlsanity.com/paper/2604.04539/) (2026)
- [Spectral Bellman Method: Unifying Representation and Exploration in RL](https://mlsanity.com/paper/2507.13181/) (2025)
- [To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2510.03207/) (2025)
- [Spectral Representation-based Reinforcement Learning](https://mlsanity.com/paper/2512.15036/) (2025)
- [WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control](https://mlsanity.com/paper/2602.14351/) (2026)
- [Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/2505.04619/) (2025)
- [Calibrated Value-Aware Model Learning with Probabilistic Environment Models](https://mlsanity.com/paper/2505.22772/) (2025)
- [Temporal Difference Learning with Constrained Initial Representations](https://mlsanity.com/paper/2602.11800/) (2026)
- [Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics](https://mlsanity.com/paper/2602.21203/) (2026)
- [Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models](https://mlsanity.com/paper/2603.15857/) (2026)
- [Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning](https://mlsanity.com/paper/2510.12096/) (2025)
- [Reward-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2603.05066/) (2026)
- [MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning](https://mlsanity.com/paper/2605.09364/) (2026)
- [Debiased Model-based Representations for Sample-efficient Continuous Control](https://mlsanity.com/paper/2605.11711/) (2026)
- [Behavior-Consistent Deep Reinforcement Learning](https://mlsanity.com/paper/2605.21214/) (2026)
- [Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding](https://mlsanity.com/paper/2508.07722/) (2025)
- [Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions](https://mlsanity.com/paper/2509.10303/) (2025)
- [A Forensic Analysis of Synthetic Data in RL: Diagnosing and Solving Algorithmic Failures in Model-Based Policy Optimization](https://mlsanity.com/paper/2510.01457/) (2025)
- [Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning](https://mlsanity.com/paper/2602.09396/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive general results across benchmarks but come at the cost of increased complexity and slow run times, limiting their broader applicability. In this paper, we attempt to find a unifying model-free deep RL algorithm that can address a diverse class of domains and problem settings. To achieve this, we leverage model-based representations that approximately linearize the value function, taking advantage of the denser task objectives used by model-based RL while avoiding the costs associated with planning or simulated trajectories. We evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a single set of hyperparameters and show a competitive performance against domain-specific and general baselines, providing a concrete step towards building general-purpose model-free deep RL algorithms.