Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness.

code for NeurIPS2021 paper on Federated Reinforcement Learning with Byzantine Resilience

Publication catalog for research on Federated RL (FRL).

[NeurIPS 2024] Code for Federated Ensemble-Directed Offline Reinforcement Learning

Experiments code for AAMAS'24 paper on "Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence"

This repo implements our paper, "Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee", which has been accepted at NuerIPS 2021.

Adaptive Confidence-Weighted Policy Aggregation: A Novel Method for Federated Reinforcement Learning