# Federated Reinforcement Learning with Environment Heterogeneity

arXiv: https://arxiv.org/abs/2204.02634 · HTML: https://mlsanity.com/paper/2204.02634/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Distral: Robust Multitask Reinforcement Learning](https://mlsanity.com/paper/1707.04175/) (2017)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations](https://mlsanity.com/paper/1308.3513/) (2013)
- [Federated User Representation Learning](https://mlsanity.com/paper/1909.12535/) (2019)
- [Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes](https://mlsanity.com/paper/1706.06544/) (2017)
- [A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning](https://mlsanity.com/paper/2006.04338/) (2020)
- [Fingerprint Policy Optimisation for Robust Reinforcement Learning](https://mlsanity.com/paper/1805.10662/) (2018)

## Built on by (most cited first, 30 of 63)

- [The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond](https://mlsanity.com/paper/2305.10697/) (2023)
- [Momentum Provably Improves Error Feedback!](https://mlsanity.com/paper/2305.15155/) (2023)
- [Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning](https://mlsanity.com/paper/2401.15273/) (2024)
- [FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence](https://mlsanity.com/paper/2204.08125/) (2022)
- [Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity](https://mlsanity.com/paper/2302.02212/) (2023)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis](https://mlsanity.com/paper/2404.08003/) (2024)
- [Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning](https://mlsanity.com/paper/2301.00944/) (2023)
- [Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices](https://mlsanity.com/paper/2402.05876/) (2024)
- [Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.03022/) (2022)
- [Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation](https://mlsanity.com/paper/2305.06446/) (2023)
- [Non-convex Stochastic Composite Optimization with Polyak Momentum](https://mlsanity.com/paper/2403.02967/) (2024)
- [The Sample-Communication Complexity Trade-off in Federated Q-Learning](https://mlsanity.com/paper/2408.16981/) (2024)
- [Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments](https://mlsanity.com/paper/2405.19499/) (2024)
- [Provably Robust Federated Reinforcement Learning](https://mlsanity.com/paper/2502.08123/) (2025)
- [SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning](https://mlsanity.com/paper/2402.04114/) (2024)
- [FedHQL: Federated Heterogeneous Q-Learning](https://mlsanity.com/paper/2301.11135/) (2023)
- [Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries](https://mlsanity.com/paper/2403.09940/) (2024)
- [Compressed Federated Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2404.10635/) (2024)
- [Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents](https://mlsanity.com/paper/2410.22908/) (2024)
- [Towards Faster Decentralized Stochastic Optimization with Communication Compression](https://mlsanity.com/paper/2405.20114/) (2024)
- [FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data](https://mlsanity.com/paper/2512.02350/) (2025)
- [Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control](https://mlsanity.com/paper/2210.01452/) (2022)
- [Towards Fast Rates for Federated and Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2409.05291/) (2024)
- [On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations](https://mlsanity.com/paper/2411.15014/) (2024)
- [Gap-Dependent Bounds for Federated Q-learning](https://mlsanity.com/paper/2502.02859/) (2025)
- [Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters](https://mlsanity.com/paper/2506.15825/) (2025)
- [Client Selection for Federated Policy Optimization with Environment Heterogeneity](https://mlsanity.com/paper/2305.10978/) (2023)
- [Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources](https://mlsanity.com/paper/2306.08364/) (2023)
- [Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents](https://mlsanity.com/paper/2512.08870/) (2025)

## Abstract (arXiv)

We study a Federated Reinforcement Learning (FedRL) problem in which n agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. We stress the constraint of environment heterogeneity, which means n environments corresponding to these n agents have different state transitions. To obtain a value function or a policy function which optimizes the overall performance in all environments, we propose two federated RL algorithms, textttQAvg and textttPAvg. We theoretically prove that these algorithms converge to suboptimal solutions, while such suboptimality depends on how heterogeneous these n environments are. Moreover, we propose a heuristic that achieves personalization by embedding the n environments into n vectors. The personalization heuristic not only improves the training but also allows for better generalization to new environments.