Deep Reinforcement learning for Board Games
Reinforcement learning (RL) is one of the key AI paradigms for the development of autonomous systems. RL allows a learning agent to solve a task based on trial-and error interactions with its environment. By observing the results of its actions, the agent can determine the optimal sequence of actions to take in order to reach some goal.
Deep learning is a popular research track within the field of machine learning. The main idea behind deep learning is to create architectures consisting of multiple layers of representations in order to learn high level abstractions. Examples are the deep neural network methods used in image processing. Starting from individual pixels, each successive layer of the network learns progressively more complex features until the highest layers are able to recognize objects in the image.
Recent research has also shown that deep learning can be used to learn useful representations for reinforcement learning tasks. This has led to a new generation of state-of-the-art algorithms that combine deep learning and reinforcement learning. One recent succes is the development of AlphaGo, a computer agent for the Go board game that plays at human world champion level. Go was considered to be the next big challenge for AI and its solution was thought to still be years away. The succes of AlphaGo demonstrates the potential and the power of the new deep RL methods.
- Introduction to deep learning
- AI-Lab deep learning resources page
- Deep reinforcement learning movie
- Playing atari with deep RL
- OpenAI Gym Deep RL Benchmark system