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Asynchronous federated reinforcement learning with policy gradient updates: Algorithm design and convergence analysis
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous
federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a …
federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a …
In-trajectory inverse reinforcement learning: Learn incrementally before an ongoing trajectory terminates
Inverse reinforcement learning (IRL) aims to learn a reward function and a corresponding
policy that best fit the demonstrated trajectories of an expert. However, current IRL works …
policy that best fit the demonstrated trajectories of an expert. However, current IRL works …