Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
Code (112)
Tasks
Atari GamesDeep Reinforcement LearningMulti-Goal Reinforcement LearningQ-LearningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Virtual Augmented Reality for Atari Reinforcement Learning
Reinforcement Learning (RL) has achieved significant milestones in the gaming domain, most notably Google DeepMind's AlphaGo defeating human Go champion Ken Jie. This victory was also made possible through the Atari Lear…
Image Segmentationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Object-sensitive Deep Reinforcement Learning
Deep reinforcement learning has become popular over recent years, showing superiority on different visual-input tasks such as playing Atari games and robot navigation. Although objects are important image elements, few w…
Atari GamesDeep Reinforcement LearningObjectObject Recognition+4Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay
This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, human checkpoint replay, cons…
Atari GamesDeep Reinforcement LearningMontezuma's Revengereinforcement-learning+2Learning Actions and Control of Focus of Attention with a Log-Polar-like Sensor
With the long-term goal of reducing the image processing time on an autonomous mobile robot in mind we explore in this paper the use of log-polar like image data with gaze control. The gaze control is not done on the Car…
Atari GamesDeep Reinforcement LearningEnhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the possible instability in the training process. We propose that a reinforcemen…
Atari Gamesreinforcement-learningReinforcement LearningTransfer Learning