Deep reinforcement learning: Difference between revisions

Revision as of 12:58, 11 June 2025

Revision as of 13:14, 21 July 2025

Line 56:

Recent developments in DRL have introduced new architectures and training strategies which aims to improving performance, efficiency, and generalization.

One key area of progress is model-based reinforcement learning, where agents learn an internal model of the environment to simulate outcomes before acting. This kind offof approach improves sample efficiency and planning. An example is the Dreamer algorithm, which learns a latent space model to train agents more efficiently in complex environments.<ref>Hafner, D. et al. "Dream to control: Learning behaviors by latent imagination." arXiv preprint arXiv:1912.01603 (2019). https://arxiv.org/abs/1912.01603</ref>

Another major innovation is the use of transformer-based architectures in DRL. Unlike traditional models that rely on recurrent or convolutional networks, transformers can model long-term dependencies more effectively. The Decision Transformer and other similar models treat RL as a sequence modeling problem, enabling agents to generalize better across tasks.<ref>Kostas, J. et al. "Transformer-based reinforcement learning agents." arXiv preprint arXiv:2209.00588 (2022). https://arxiv.org/abs/2209.00588</ref>