Deep reinforcement learning from self-play in imperfect-information games
J Heinrich, D Silver - arXiv preprint arXiv:1603.01121, 2016 - arxiv.org
Many real-world applications can be described as large-scale games of imperfect
information. To deal with these challenging domains, prior work has focused on computing …
information. To deal with these challenging domains, prior work has focused on computing …
Reinforcement learning for personalization: A systematic literature review
The major application areas of reinforcement learning (RL) have traditionally been game
playing and continuous control. In recent years, however, RL has been increasingly applied …
playing and continuous control. In recent years, however, RL has been increasingly applied …
A multiagent competitive gaming platform to address societal challenges
The shift toward sustainable electricity systems is one of the grand challenges of the 21st
century. Decentralized production from renewable sources, electric mobility, and related …
century. Decentralized production from renewable sources, electric mobility, and related …
Summarizing agent strategies
Intelligent agents and AI-based systems are becoming increasingly prevalent. They support
people in different ways, such as providing users with advice, working with them to achieve …
people in different ways, such as providing users with advice, working with them to achieve …
Continual reinforcement learning deployed in real-life using policy distillation and sim2real transfer
We focus on the problem of teaching a robot to solve tasks presented sequentially, ie, in a
continual learning scenario. The robot should be able to solve all tasks it has encountered …
continual learning scenario. The robot should be able to solve all tasks it has encountered …
[PDF][PDF] Recurrent deep multiagent q-learning for autonomous brokers in smart grid.
The broker mechanism is widely applied to serve for interested parties to derive long-term
policies in order to reduce costs or gain profits in smart grid. However, a broker is faced with …
policies in order to reduce costs or gain profits in smart grid. However, a broker is faced with …
Efficiently detecting switches against non-stationary opponents
P Hernandez-Leal, Y Zhan, ME Taylor… - Autonomous Agents and …, 2017 - Springer
Interactions in multiagent systems are generally more complicated than single agent ones.
Game theory provides solutions on how to act in multiagent scenarios; however, it assumes …
Game theory provides solutions on how to act in multiagent scenarios; however, it assumes …
[PDF][PDF] Agent strategy summarization
Intelligent agents and AI-based systems are becoming increasingly prevalent. They support
people in different ways, such as providing users with advice, working with them to achieve …
people in different ways, such as providing users with advice, working with them to achieve …
Hierarchical decision making in electricity grid management
The power grid is a complex and vital system that necessitates careful reliability
management. Managing the grid is a difficult problem with multiple time scales of decision …
management. Managing the grid is a difficult problem with multiple time scales of decision …