Abstract: This research summarizes the major developments of the Deep Deterministic Policy Gradient (DDPG) in reinforcement learning Motivated by the ideas, of Deep Q-networks, DDPG has proven capable of confronting more complex problems involving continuous action spaces. The core of DDPG lies in its actor-critic architecture, which enables the learning of highly competitive policies. By leveraging neural network function approximations, it can efficiently operate in large state and action spaces. DDPG has found practical applications across various real-world domains. However, like many model-free reinforcement learning methods, DDPG still faces the challenge of requiring a large number of training steps.
Keywords: RL, DDPG, DQN, NN.
[1]
Arulkumaran, K., Deisenroth, M.P., Brundage, M. Bharath, A.A.: Deep Reinforcement Learning: A Brief Survey. Ieee Signal Processing Magazine 34(6),26–38 (2017)37.
[2]
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., Meger, D.: Deep Reinforcement Learning That Matters. In: Proceedings Of The Aaai Conference On Artificial Intelligence, Vol. 32.
[3]
Dulac-Arnold, G., Mankowitz, D., Hester, T.: Challenges Of Real-World Reinforcement Learning. Arxiv Preprint Arxiv:1904.12901 (2019).
[4]
Rehman, A., Saba, T., Haseeb, K., Alam, T., Lloret, J.: Sustainability Model For The Internet Of Health Things (Ioht) Using Reinforcement Learning With Mobile Edge Secured Services. Sustainability 14(19), 12185 (2022).
[5]
Zhao, K., Song, J., Luo, Y., Liu, Y.: Research On Game-Playing Agents Based On Deep Reinforcement Learning. Robotics 11(2), 35 (2022)...
