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Welcome back to the exciting world of deep learning! Today, we delve into the fascinating realm of deep reinforcement learning. We’ll explore concepts such as deep cue learning and its groundbreaking application – human control through deep reinforcement learning. This breakthrough was achieved by Google DeepMind, where a neural network successfully played Atari games. Let’s dive in!
Contents
Deep Cue Learning: Mastering Atari Games
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Deep reinforcement learning allows us to directly learn the action value function using a deep network. In the case of Atari games, the inputs are three subsequent video frames from the game. Processed by a deep network, these frames aid in producing the best next action. The architecture consists of convolutional layers for frame processing and fully connected layers for decision-making.
The Architecture: Processing the Game Frames
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The architecture comprises convolutional layers that process the input frames. These frames pass through fully connected layers, which ultimately produce the desired output. In Atari games, the action set is limited. You can choose between no action, eight directions, a fire button, or eight directions plus the fire button. This limited domain enables training of the system.
Training with Q-Learning
The deep network applies Q-learning, where the current state of the game consists of the current frame plus three previous frames as an image stack. With 18 outputs associated with different actions, each output estimates the action for the given input. Instead of having a label and cost function, the network updates with respect to maximizing future rewards.
Overcoming Challenges: Target Network and Experience Replay
To stabilize the learning process, two techniques are employed. First, a target network is introduced. Every few steps, the weights of the action value network are copied to this target network. The target network’s output, denoted as Q-bar, is used as a stabilized target for maximization. Second, experience replay is utilized, which reduces correlation between updates. By randomly drawing samples from a replay memory, the system considers various game situations, thereby increasing stability.
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Experience the Marvel: Atari Breakout
To witness the capabilities of deep reinforcement learning, observe the example of Atari Breakout. Initially, the agent performs poorly, but after several iterations of training, it learns to follow the ball with the paddle and reflect it. As the training progresses, the system even discovers a unique strategy – aiming to hit bricks on the left side and bouncing the ball behind the remaining bricks. This showcases the system’s ability to exploit the game’s weaknesses.
The AlphaGo Breakthrough: Mastering Go
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Let’s shift our focus to AlphaGo, the revolutionary AI program that conquered the game of Go. Considered more complex than chess due to its vast number of possible moves and states, Go was thought to be unsolvable without significantly faster computers. However, AlphaGo proved otherwise. Developed by DeepMind, it combines deep neural networks, Monte Carlo tree search, supervised learning, and reinforcement learning.
Monte Carlo Tree Search: Exploring Promising Paths
In Monte Carlo tree search, the search tree expands by examining different future moves. By expanding valuable states over multiple moves into the future, the system discovers future states with higher values. This method helps identify promising actions and improve overall performance.
The Power of Deep Neural Networks
Three deep neural networks play a crucial role in AlphaGo’s success. The policy network suggests the next move during the search. The value network assesses the chances of winning based on the current board situation. The rollout policy network aids in quick action selection during the rollout phase. These networks are trained using massive datasets and reinforcement learning, significantly enhancing AlphaGo’s performance.
Conclusion
In this adventure into the realm of deep reinforcement learning, we explored the groundbreaking applications of deep cue learning and the incredible achievements of Google DeepMind’s AlphaGo. By combining deep neural networks, tree search, and reinforcement learning, we witnessed the incredible potential of AI in gaming. If you’re intrigued by this subject, we highly recommend diving into the comprehensive book “Reinforcement Learning” by Richard Sutton and Andrew Barto.
FAQs
Q: What is a policy?
A: A policy is a function that determines the next action to be taken in a given state.
Q: What are value functions?
A: Value functions estimate the expected future rewards or values of being in a particular state or taking a specific action.
Q: What is the exploitation versus exploration dilemma?
A: The exploitation versus exploration dilemma refers to the trade-off between exploiting known information for immediate gains and exploring unknown information to maximize long-term rewards.
Resources
Thank you for joining us on this captivating journey through the realm of deep reinforcement learning. Stay tuned for more exciting insights into the world of technology. For more articles like this, visit Techal.
