The document presents a detailed account of the author's experiences and experiments with deep reinforcement learning (DRL), particularly focusing on the game 'Montezuma's Revenge' and the A3C+ methodology from DeepMind. It outlines various challenges faced in training agents to achieve human-level scores, such as handling reward distribution and implementing pseudo-reward mechanisms. The author shares insights on improvements made to the A3C framework and the significance of intrinsic motivation to encourage exploration in complex gaming environments.
This document introduces the deep reinforcement learning model 'A3C' by Japanese.
Original literature is "Asynchronous Methods for Deep Reinforcement Learning" written by V. Mnih, et. al.
This document discusses benchmarking deep learning frameworks like Chainer. It begins by defining benchmarks and their importance for framework developers and users. It then examines examples like convnet-benchmarks, which objectively compares frameworks on metrics like elapsed time. It discusses challenges in accurately measuring elapsed time for neural network functions, particularly those with both Python and GPU components. Finally, it introduces potential solutions like Chainer's Timer class and mentions the DeepMark benchmarks for broader comparisons.
This document summarizes an internship project using deep reinforcement learning to develop an agent that can automatically park a car simulator. The agent takes input from virtual cameras mounted on the car and uses a DQN network to learn which actions to take to reach a parking goal. Several agent configurations were tested, with the three-camera subjective view agent showing the most success after modifications to the reward function and task difficulty via curriculum learning. While the agent could sometimes learn to park, the learning was not always stable, indicating further refinement is needed to the deep RL approach for this automatic parking task.
The document summarizes a meetup discussing deep learning and Docker. It covered Yuta Kashino introducing BakFoo and his background in astrophysics and Python. The meetup discussed recent advances in AI like AlphaGo, generative adversarial networks, and neural style transfer. It provided an overview of Chainer and arXiv papers. The meetup demonstrated Chainer 1.3, NVIDIA drivers, and Docker for deep learning. It showed running a TensorFlow tutorial using nvidia-docker and provided Dockerfile examples and links to resources.
This document presents mathematical formulas for calculating gradients and updates in reinforcement learning. It defines a formula for calculating the gradient of a value function with respect to its parameters, a formula for calculating the gradient of a policy based on the reward and value, and a formula for calculating the gradient of a parameter vector that is a weighted combination of its previous value and the policy gradient.
The document outlines the updates from Chainer versions v1.8.0 to v1.10.0, highlighting new features such as improved caffefunction support, weight initialization capabilities, and enhanced ndarray handling. It discusses the introduction of new functions and links, modifications to existing functionalities, and plans for more frequent minor releases due to a backlog of pull requests. Future updates are planned, including a major version release aimed at enhancing performance and usability through various improvements.
This document provides an overview of using various Python libraries and tools for image recognition, including OpenCV for image processing, Selenium and Hulu for web scraping, Keras and TensorFlow for building convolutional neural networks, and Bottle as a web framework. Code examples are given for preprocessing images, creating a CNN model in Keras to classify images into 10 classes using the CIFAR-10 dataset, and exporting the trained model.
The document presents a detailed account of the author's experiences and experiments with deep reinforcement learning (DRL), particularly focusing on the game 'Montezuma's Revenge' and the A3C+ methodology from DeepMind. It outlines various challenges faced in training agents to achieve human-level scores, such as handling reward distribution and implementing pseudo-reward mechanisms. The author shares insights on improvements made to the A3C framework and the significance of intrinsic motivation to encourage exploration in complex gaming environments.
This document introduces the deep reinforcement learning model 'A3C' by Japanese.
Original literature is "Asynchronous Methods for Deep Reinforcement Learning" written by V. Mnih, et. al.
This document discusses benchmarking deep learning frameworks like Chainer. It begins by defining benchmarks and their importance for framework developers and users. It then examines examples like convnet-benchmarks, which objectively compares frameworks on metrics like elapsed time. It discusses challenges in accurately measuring elapsed time for neural network functions, particularly those with both Python and GPU components. Finally, it introduces potential solutions like Chainer's Timer class and mentions the DeepMark benchmarks for broader comparisons.
This document summarizes an internship project using deep reinforcement learning to develop an agent that can automatically park a car simulator. The agent takes input from virtual cameras mounted on the car and uses a DQN network to learn which actions to take to reach a parking goal. Several agent configurations were tested, with the three-camera subjective view agent showing the most success after modifications to the reward function and task difficulty via curriculum learning. While the agent could sometimes learn to park, the learning was not always stable, indicating further refinement is needed to the deep RL approach for this automatic parking task.
The document summarizes a meetup discussing deep learning and Docker. It covered Yuta Kashino introducing BakFoo and his background in astrophysics and Python. The meetup discussed recent advances in AI like AlphaGo, generative adversarial networks, and neural style transfer. It provided an overview of Chainer and arXiv papers. The meetup demonstrated Chainer 1.3, NVIDIA drivers, and Docker for deep learning. It showed running a TensorFlow tutorial using nvidia-docker and provided Dockerfile examples and links to resources.
This document presents mathematical formulas for calculating gradients and updates in reinforcement learning. It defines a formula for calculating the gradient of a value function with respect to its parameters, a formula for calculating the gradient of a policy based on the reward and value, and a formula for calculating the gradient of a parameter vector that is a weighted combination of its previous value and the policy gradient.
The document outlines the updates from Chainer versions v1.8.0 to v1.10.0, highlighting new features such as improved caffefunction support, weight initialization capabilities, and enhanced ndarray handling. It discusses the introduction of new functions and links, modifications to existing functionalities, and plans for more frequent minor releases due to a backlog of pull requests. Future updates are planned, including a major version release aimed at enhancing performance and usability through various improvements.
This document provides an overview of using various Python libraries and tools for image recognition, including OpenCV for image processing, Selenium and Hulu for web scraping, Keras and TensorFlow for building convolutional neural networks, and Bottle as a web framework. Code examples are given for preprocessing images, creating a CNN model in Keras to classify images into 10 classes using the CIFAR-10 dataset, and exporting the trained model.