TD3 addresses the problem of overestimated value estimates and suboptimal policies in value-based reinforcement learning methods, especially in deep Q-learning approaches. It introduces a novel algorithm based on Double Q-learning that employs two critics to reduce overestimation. It also introduces target networks and suggests delaying policy updates to reduce errors and enhance performance, resulting in better and faster learning.
I would appreciate your opinion on whether adding TD3 to the repository would be a valuable contribution.
Hi,
I am interested in adding the implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3) to the Keras examples repository.
TD3 addresses the problem of overestimated value estimates and suboptimal policies in value-based reinforcement learning methods, especially in deep Q-learning approaches. It introduces a novel algorithm based on Double Q-learning that employs two critics to reduce overestimation. It also introduces target networks and suggests delaying policy updates to reduce errors and enhance performance, resulting in better and faster learning.
I would appreciate your opinion on whether adding TD3 to the repository would be a valuable contribution.
Thank you for your time.