Special Issue "Advances in Reinforcement Learning"
Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 5072
Interests: deep learning; reinforcement learning; computer vision; NLP; optimization
Reinforcement Learning (RL), in which the agents learn by interacting with the environment, is one of the most exciting areas of Artificial Intelligence. Unlike other AI paradigms of supervised and unsupervised learning, no predisposed intuition, data, or supervision is necessary in RL. Starting from the foundational work of the Bellman equations, many RL algorithms have been proposed in the last few years, and the success of RL has been demonstrated in many practical applications in the fields of robotics, autonomous vehicles, communication systems, game playing, finance, healthcare, adaptive decision control, among others. Even though considerable work on algorithmic and mathematical formulations related to single-agent RL systems has led to impressive results in different domains, Multi-Agent RL (MARL) is still in its infancy. Some of the challenges yet to be resolved, both for single- and Multi-Agent RL systems, include real-time adaptation to nonstationary or stochastic environments, high-dimensional continuous state and action spaces, adversarial RL including both attacks and defenses, partial observability of the environment, RL under interference or noisy environments, and safety control. To provide some of the solutions to the challenging problems of RL, we propose this Special Issue on “Advances in Reinforcement Learning”. With this aim, we invite papers in both theoretical and applied research areas related to RL and MARL. We believe this Special Issue will contribute to advancing the state of the art in reinforcement learning.
Prof. Dr. Ausif Mahmood
Manuscript Submission Information
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- reinforcement learning
- multi-agent reinforcement learning
- Markov decision process
- value iteration
- policy gradients
- learning to learn
- deep Q learning
- Markov game
- deep reinforcement learning