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Federated Reinforcement Learning with Constraint Heterogeneity

Hao Jin, Liangyu Zhang, Zhihua Zhang

TL;DR

This work tackles federated reinforcement learning when agents observe different constraint signals, introducing a federated primal-dual framework with local Lagrange functions and periodic policy aggregation. It provides two algorithmic instances: FedNPG for tabular settings with a provable $\tilde{O}(1/\sqrt{T})$ convergence rate and FedPPO for deep-network-based tasks. Theoretical results quantify the trade-offs between sample complexity, constraint satisfaction, and approximation bias, while experiments on tabular and continuous-control tasks show that the proposed methods closely approach omniscient baselines while satisfying all constraints. The approach enables privacy-preserving, multi-constraint RL in domains like LLM fine-tuning and healthcare, where constraint signals are costly or distributed across devices.

Abstract

We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while $N$ training agents are located in $N$ different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce $N$ local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, {FedNPG} and {FedPPO}. We show that FedNPG achieves global convergence with an $\tilde{O}(1/\sqrt{T})$ rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.

Federated Reinforcement Learning with Constraint Heterogeneity

TL;DR

This work tackles federated reinforcement learning when agents observe different constraint signals, introducing a federated primal-dual framework with local Lagrange functions and periodic policy aggregation. It provides two algorithmic instances: FedNPG for tabular settings with a provable convergence rate and FedPPO for deep-network-based tasks. Theoretical results quantify the trade-offs between sample complexity, constraint satisfaction, and approximation bias, while experiments on tabular and continuous-control tasks show that the proposed methods closely approach omniscient baselines while satisfying all constraints. The approach enables privacy-preserving, multi-constraint RL in domains like LLM fine-tuning and healthcare, where constraint signals are costly or distributed across devices.

Abstract

We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while training agents are located in different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, {FedNPG} and {FedPPO}. We show that FedNPG achieves global convergence with an rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.
Paper Structure (34 sections, 13 theorems, 82 equations, 2 figures, 3 algorithms)

This paper contains 34 sections, 13 theorems, 82 equations, 2 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Constrained Reinforcement Learning
  3. Constrained Markov decision processes
  4. Primal-dual policy optimization methods
  5. Federated Reinforcement Learning with constraint heterogeneity
  6. Local Lagrange functions from $L_0(\lambda,\theta)$
  7. Periodic communication of local policies
  8. Theoretical Analysis
  9. The FedNPG algorithm
  10. Technical assumptions
  11. Main results and discussions
  12. Empirical Study
  13. The Set-up
  14. Performance of FedNPG
  15. Performance of FedPPO
  16. ...and 19 more sections

Key Result

Lemma 4.1

If Assumption Assumption_full_policy_class and Assumption Assumption_Slater are true, we have:

Figures (2)

  • Figure 1: Comparison between baselines and FedPPO in CartPole (first row), Acrobot (second row) and Inverted-Pendulum (third row): we depict the mean as line, standard error as shadow, and constraint thresholds as red lines.
  • Figure 2: The gridworld in WindyCliff with size of $4\times 10$.

Theorems & Definitions (26)

  • Lemma 4.1: Strong duality and boundedness of dual variables
  • Theorem 4.1
  • Lemma D.1: Performance difference lemma
  • proof
  • Lemma D.2: Lipschitz values
  • proof
  • Lemma D.3: Strong duality and bounded dual variables
  • proof
  • Lemma D.4: Constraint violation
  • proof
  • ...and 16 more