# Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning

arXiv: https://arxiv.org/abs/2602.09396 · HTML: https://mlsanity.com/paper/2602.09396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## 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, 25 of 25)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [CURL: Contrastive Unsupervised Representations for Reinforcement Learning](https://mlsanity.com/paper/2004.04136/) (2020)
- [Learning Invariant Representations for Reinforcement Learning without Reconstruction](https://mlsanity.com/paper/2006.10742/) (2020)
- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [Data-Efficient Reinforcement Learning with Self-Predictive Representations](https://mlsanity.com/paper/2007.05929/) (2020)
- [Bridging State and History Representations: Understanding Self-Predictive RL](https://mlsanity.com/paper/2401.08898/) (2024)
- [Towards General-Purpose Model-Free Reinforcement Learning](https://mlsanity.com/paper/2501.16142/) (2025)
- [The Challenges of Continuous Self-Supervised Learning](https://mlsanity.com/paper/2203.12710/) (2022)
- [A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning](https://mlsanity.com/paper/2104.13844/) (2021)
- [Streaming Deep Reinforcement Learning Finally Works](https://mlsanity.com/paper/2410.14606/) (2024)
- [Learning Representations for Pixel-based Control: What Matters and Why?](https://mlsanity.com/paper/2111.07775/) (2021)
- [Universal Successor Features for Transfer Reinforcement Learning](https://mlsanity.com/paper/2001.04025/) (2020)
- [Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers](https://mlsanity.com/paper/2411.15370/) (2024)
- [Harnessing Discrete Representations For Continual Reinforcement Learning](https://mlsanity.com/paper/2312.01203/) (2023)
- [Random Representations Outperform Online Continually Learned Representations](https://mlsanity.com/paper/2402.08823/) (2024)
- [Elephant Neural Networks: Born to Be a Continual Learner](https://mlsanity.com/paper/2310.01365/) (2023)
- [Learning Successor Features the Simple Way](https://mlsanity.com/paper/2410.22133/) (2024)
- [Learning from Streaming Video with Orthogonal Gradients](https://mlsanity.com/paper/2504.01961/) (2025)
- [Deep Reinforcement Learning with Gradient Eligibility Traces](https://mlsanity.com/paper/2507.09087/) (2025)
- [Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX](https://mlsanity.com/paper/2510.01764/) (2025)
- [A Study of Continual Learning Methods for Q-Learning](https://mlsanity.com/paper/2206.03934/) (2022)

## Abstract (arXiv)

In streaming Reinforcement Learning (RL), transitions are observed and discarded immediately after a single update. While this minimizes resource usage for on-device applications, it makes agents notoriously sample-inefficient, since value-based losses alone struggle to extract meaningful representations from transient data. We propose extending Self-Predictive Representations (SPR) to the streaming pipeline to maximize the utility of every observed frame. However, due to the highly correlated samples induced by the streaming regime, naively applying this auxiliary loss results in training instabilities. Thus, we introduce orthogonal gradient updates relative to the momentum target and resolve gradient conflicts arising from streaming-specific optimizers. Validated across the Atari, MinAtar, and Octax suites, our approach systematically outperforms existing streaming baselines. Latent-space analysis, including t-SNE visualizations and effective-rank measurements, confirms that our method learns significantly richer representations, bridging the performance gap caused by the absence of a replay buffer, while remaining efficient enough to train on just a few CPU cores.