Deep multiagent reinforcement learning: Challenges and directions
This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …
combination of deep neural networks with RL has gained increased traction in recent years …
From chess and atari to starcraft and beyond: How game ai is driving the world of ai
This paper reviews the field of Game AI, which not only deals with creating agents that can
play a certain game, but also with areas as diverse as creating game content automatically …
play a certain game, but also with areas as diverse as creating game content automatically …
High-accuracy model-based reinforcement learning, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems from game playing and robotics have been …
complex sequential decision making problems from game playing and robotics have been …
Deep model-based reinforcement learning for high-dimensional problems, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …
complex sequential decision making problems have been solved in tasks such as game …
Adaptive warm-start MCTS in AlphaZero-like deep reinforcement learning
AlphaZero has achieved impressive performance in deep reinforcement learning by utilizing
an architecture that combines search and training of a neural network in self-play. Many …
an architecture that combines search and training of a neural network in self-play. Many …
Tackling morpion solitaire with alphazero-like ranked reward reinforcement learning
Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to
its large state space (on the order of the game of Go) traditional search algorithms, such as …
its large state space (on the order of the game of Go) traditional search algorithms, such as …
Analysis of hyper-parameters for AlphaZero-like deep reinforcement learning
The landmark achievements of AlphaGo Zero have created great research interest into self-
play in reinforcement learning. In self-play, Monte Carlo Tree Search (MCTS) is used to train …
play in reinforcement learning. In self-play, Monte Carlo Tree Search (MCTS) is used to train …
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
The landmark achievements of AlphaGo Zero have created great research interest into self-
play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a deep …
play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a deep …
Transfer learning and curriculum learning in Sokoban
Transfer learning can speed up training in machine learning, and is regularly used in
classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new …
classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new …
Deep reinforcement learning, a textbook
A Plaat - arXiv preprint arXiv:2201.02135, 2022 - arxiv.org
Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …
achieved in activities as diverse as autonomous driving, game playing, molecular …