Deep reinforcement learning: Difference between revisions

Revision as of 13:14, 21 July 2025

Revision as of 13:15, 21 July 2025

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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>

In addition, research into open-ended learning has led to the creation of  capable agents that are able to solve a range of tasks without task-specific tuning. Similar systems like the ones that are developed by OpenAI show that agents trained in diverse, evolving environments can generalize across new challenges, moving toward more adaptive and flexible intelligence.<ref>OpenAI et al. "Open-ended learning leads to generally capable agents." arXiv preprint arXiv:2302.06622 (2023). https://arxiv.org/abs/2302.06622</ref>

=== Future directions ===