# In value-based deep reinforcement learning, a pruned network is a good network

arXiv: https://arxiv.org/abs/2402.12479 · HTML: https://mlsanity.com/paper/2402.12479/

## Key facts

- Cited by 39 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 61 of its 87 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## 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 61)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [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)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [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)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [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)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Rigging the Lottery: Making All Tickets Winners](https://mlsanity.com/paper/1911.11134/) (2019)
- [The State of Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1902.09574/) (2019)
- [Implicit Quantile Networks for Distributional Reinforcement Learning](https://mlsanity.com/paper/1806.06923/) (2018)
- [Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science](https://mlsanity.com/paper/1707.04780/) (2017)
- [Noisy Networks for Exploration](https://mlsanity.com/paper/1706.10295/) (2017)
- [The Primacy Bias in Deep Reinforcement Learning](https://mlsanity.com/paper/2205.07802/) (2022)
- [Revisiting Fundamentals of Experience Replay](https://mlsanity.com/paper/2007.06700/) (2020)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [Deep Reinforcement Learning and the Deadly Triad](https://mlsanity.com/paper/1812.02648/) (2018)
- [The Dormant Neuron Phenomenon in Deep Reinforcement Learning](https://mlsanity.com/paper/2302.12902/) (2023)
- [Understanding plasticity in neural networks](https://mlsanity.com/paper/2303.01486/) (2023)

## Built on by (most cited first, 30 of 39)

- [Stop Regressing: Training Value Functions via Classification for Scalable Deep RL](https://mlsanity.com/paper/2403.03950/) (2024)
- [Plasticity Loss in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2411.04832/) (2024)
- [Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.15544/) (2025)
- [Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn](https://mlsanity.com/paper/2506.00592/) (2025)
- [On the consistency of hyper-parameter selection in value-based deep reinforcement learning](https://mlsanity.com/paper/2406.17523/) (2024)
- [Mixture of Experts in a Mixture of RL settings](https://mlsanity.com/paper/2406.18420/) (2024)
- [Neuroplastic Expansion in Deep Reinforcement Learning](https://mlsanity.com/paper/2410.07994/) (2024)
- [Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL](https://mlsanity.com/paper/2410.01930/) (2024)
- [Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning](https://mlsanity.com/paper/2505.24061/) (2025)
- [The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks](https://mlsanity.com/paper/2506.03404/) (2025)
- [Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents](https://mlsanity.com/paper/2510.13704/) (2025)
- [Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization](https://mlsanity.com/paper/2410.00051/) (2024)
- [Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning](https://mlsanity.com/paper/2506.17204/) (2025)
- [Active Fine-Tuning of Multi-Task Policies](https://mlsanity.com/paper/2410.05026/) (2024)
- [Hadamax Encoding: Elevating Performance in Model-Free Atari](https://mlsanity.com/paper/2505.15345/) (2025)
- [Eau De Q-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning](https://mlsanity.com/paper/2503.01437/) (2025)
- [Impoola: The Power of Average Pooling for Image-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2503.05546/) (2025)
- [The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.13672/) (2025)
- [Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning](https://mlsanity.com/paper/2509.22335/) (2025)
- [CALE: Continuous Arcade Learning Environment](https://mlsanity.com/paper/2410.23810/) (2024)
- [Stable Deep Reinforcement Learning via Isotropic Gaussian Representations](https://mlsanity.com/paper/2602.19373/) (2026)
- [Plasticity-Aware Mixture of Experts for Learning Under QoE Shifts in Adaptive Video Streaming](https://mlsanity.com/paper/2504.09906/) (2025)
- [Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning](https://mlsanity.com/paper/2505.17749/) (2025)
- [Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2508.06871/) (2025)
- [The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning](https://mlsanity.com/paper/2604.01913/) (2026)
- [Hadamard Representation: Scaffolding Performance Across Model-free RL](https://mlsanity.com/paper/2406.09079/) (2024)
- [Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems](https://mlsanity.com/paper/2503.15172/) (2025)
- [Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension](https://mlsanity.com/paper/2510.01105/) (2025)
- [Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning](https://mlsanity.com/paper/2510.12096/) (2025)
- [Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning](https://mlsanity.com/paper/2411.07759/) (2024)

## Abstract (arXiv)

Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables value-based agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters.