chapter3.pdf

Notes on Chapter 3: Deep Learning 1 Deep Learning 1.1 Core Concepts Deep Learning: A subset of machine learning that involves training neural networks with multiple layers (deep networks) to model complex patterns in data. Deep learning is particularly eff...

Notes on Chapter 3: Deep Learning 1 Deep Learning 1.1 Core Concepts Deep Learning: A subset of machine learning that involves training neural networks with multiple layers (deep networks) to model complex patterns in data. Deep learning is particularly effective for tasks involving high-dimensional data such as images, audio, and text. Neural Networks: Computational models inspired by the human brain, consisting of intercon- nected layers of nodes (neurons) that process input data and learn patterns through training. 1.2 Core Problem Core Problem in Deep Learning: The main challenge in deep learning is to train deep neu- ral networks effectively to generalize well on unseen data. This involves optimizing the network parameters to minimize a loss function, ensuring stability and convergence during training, and dealing with issues such as overfitting and vanishing gradients. 1.3 Core Algorithm Gradient Descent: A key optimization algorithm used in deep learning to minimize the loss function by iteratively updating the network parameters in the direction of the negative gradient of the loss. θ ← θ − α∇θ J(θ) where θ are the parameters, α is the learning rate, and J(θ) is the loss function. 1.4 End-to-end Learning End-to-end Learning: A training approach where raw input data is directly mapped to the desired output through a single, integrated process, typically using deep neural networks. 2 Large, High-Dimensional Problems Large, high-dimensional problems are characterized by vast and complex state and action spaces, which are common in applications such as video games and real-time strategy games. 2.1 Atari Arcade Games Atari Games: These games serve as a benchmark in deep reinforcement learning research. They present a variety of tasks that are challenging for AI due to their high-dimensional state spaces (e.g., raw pixel inputs) and complex dynamics. 2.2 Real-Time Strategy and Video Games Real-Time Strategy (RTS) Games: These games involve managing resources, strategic plan- ning, and real-time decision-making, making them more complex than arcade games. They feature larger state and action spaces, requiring sophisticated AI techniques. 1 3 Deep Value-Based Agents Deep value-based agents use deep learning to approximate value functions, enabling them to handle large and high-dimensional state spaces. 3.1 Generalization of Large Problems with Deep Learning Generalization is crucial for deep learning models to perform well on unseen data, especially in large, high-dimensional problems. 3.1.1 Minimizing Supervised Target Loss Supervised Target Loss: In supervised learning, the loss function measures the difference be- tween predicted outputs and actual targets. Common loss functions include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. n 1X MSE = (yi − ŷi )2 n i=1 where yi are the true values and ŷi are the predicted values. 3.1.2 Bootstrapping Q-Values Q-Learning: A reinforcement learning algorithm that updates Q-values using the Bellman equa- tion. Bootstrapping refers to using current estimates to update future estimates.   Q(s, a) ← Q(s, a) + α r + γ max ′ Q(s′ , a′ ) − Q(s, a) a 3.1.3 Deep Reinforcement Learning Target-Error Target-Error: In deep reinforcement learning, target-error refers to the difference between pre- dicted Q-values and target Q-values used for training the network. Reducing this error is essential for stable learning. 3.2 Three Challenges Deep value-based agents face three main challenges: coverage, correlation, and convergence. 3.2.1 Coverage Coverage: Ensuring that the agent explores all relevant parts of the state space to learn a com- prehensive policy. Inadequate coverage can lead to poor generalization and suboptimal policies. 3.2.2 Correlation Correlation: In sequential decision problems, consecutive states are often correlated, leading to inefficient learning and convergence issues. Techniques like experience replay help decorrelate the training data. 3.2.3 Convergence Convergence: Ensuring that the learning algorithm converges to an optimal policy. This involves addressing issues like the deadly triad, which includes function approximation, bootstrapping, and off-policy learning. 3.2.4 Deadly Triad Deadly Triad: The combination of function approximation, bootstrapping, and off-policy learning can lead to instability and divergence in reinforcement learning algorithms. 2 3.3 Stable Deep Value-Based Learning To achieve stable learning in deep value-based agents, several techniques are used, such as decorrelat- ing states, infrequent updates of target weights, and hands-on practice with examples like DQN and Breakout. 3.3.1 Decorrelating States Experience Replay: A technique where the agent stores past experiences (state, action, reward, next state) in a replay buffer and samples from this buffer to break correlations in the training data. 3.3.2 Infrequent Updates of Target Weights Target Network: A network used to provide stable target values in Q-learning. The weights of the target network are updated less frequently than the main network, which helps in stabilizing learning. 3.3.3 Hands On: DQN and Breakout Gym Example DQN (Deep Q-Network): Combines Q-learning with deep neural networks to handle high- dimensional state spaces, such as raw pixels from games. 3.4 Improving Exploration Exploration is crucial in reinforcement learning to ensure that the agent can discover optimal policies. Various methods help improve exploration. 3.4.1 Overestimation Overestimation: A problem in Q-learning where the estimated Q-values can be overly optimistic. This can be mitigated using techniques like Double Q-learning. 3.4.2 Prioritized Experience Replay Prioritized Experience Replay: An extension of experience replay where transitions are sam- pled based on their TD error, giving priority to experiences that are more surprising or informative. 3.4.3 Advantage Function Advantage Function: A measure of the relative value of an action compared to the average value of all actions in that state. It helps reduce variance in policy gradient methods. A(s, a) = Q(s, a) − V (s) 3.4.4 Distributional Methods Distributional RL: An approach where the distribution of possible future rewards is modeled rather than just the expected value. This provides a richer representation of uncertainty. 3.4.5 Noisy DQN Noisy DQN: An extension of DQN where noise is added to the parameters of the network to encourage exploration. 4 Atari 2600 Environments Atari 2600 games are commonly used for benchmarking reinforcement learning algorithms due to their diverse and challenging environments. 3 4.1 Network Architecture Network Architecture: The structure of the neural network used in deep reinforcement learning, typically involving convolutional layers for processing visual inputs from Atari games. 4.2 Benchmarking Atari Benchmarking: Evaluating the performance of reinforcement learning algorithms on a standard set of Atari 2600 games to compare effectiveness and efficiency. 5 Conclusion 6 Summary and Further Reading 6.1 Summary Deep learning and reinforcement learning can be combined to solve large, high-dimensional prob- lems. Techniques like experience replay, target networks, and prioritized experience replay are essential for stable and efficient learning. 6.2 Further Reading Explore additional resources on deep reinforcement learning, such as research papers, books, and online courses to gain a deeper understanding of the field. 7 Exercise 1. What is Gym? Answer: Gym is a toolkit for developing and comparing reinforcement learning algorithms, providing environments for training and testing RL agents. 2. What are the Stable Baselines? Answer: Stable Baselines are a set of reliable implementations of reinforcement learning algorithms in Python, designed to provide stable and efficient learning. 3. The loss function of DQN uses the Q-function as target. What is a consequence? Answer: A consequence is that it can lead to overestimation bias in the Q-values, potentially resulting in suboptimal policies. 4. Why is the exploration/exploitation trade-off central in reinforcement learning? Answer: It is central because the agent needs to balance exploring new actions to discover better rewards and exploiting known actions to maximize rewards. 5. Name one simple exploration/exploitation method. Answer: ϵ-greedy is a simple exploration/exploitation method. 6. What is bootstrapping? Answer: Bootstrapping is a method in reinforcement learning where current estimates are used to update future estimates. 7. Describe the architecture of the neural network in DQN. 4  Answer: The neural network in DQN typically consists of convolutional layers followed by fully connected layers to process high-dimensional input like raw pixel data from games. 8. Why is deep reinforcement learning more susceptible to unstable learning than deep supervised learning? Answer: Deep reinforcement learning is more susceptible due to the combination of function approximation, bootstrapping, and the use of sequentially correlated data. 9. What is the deadly triad? Answer: The deadly triad refers to the combination of function approximation, bootstrap- ping, and off-policy learning that can lead to instability and divergence in reinforcement learning algorithms. 10. How does function approximation reduce stability of Q-learning? Answer: Function approximation can introduce estimation errors that accumulate over time, reducing the stability and leading to divergence. 11. What is the role of the replay buffer? Answer: The replay buffer stores past experiences to break correlations in the training data and improve learning stability. 12. How can correlation between states lead to local minima? Answer: Correlation between states can cause the agent to get stuck in suboptimal policies by repeatedly reinforcing similar experiences. 13. Why should the coverage of the state space be sufficient? Answer: Sufficient coverage ensures the agent explores all relevant parts of the state space to learn a comprehensive and optimal policy. 14. What happens when deep reinforcement learning algorithms do not converge? Answer: When they do not converge, the agent’s performance becomes unstable and unpre- dictable, failing to learn a useful policy. 15. How large is the state space of chess estimated to be? 1047 , 10170 , or 101685 ? Answer: The state space of chess is estimated to be 1047. 16. How large is the state space of Go estimated to be? 1047 , 10170 , or 101685 ? Answer: The state space of Go is estimated to be 10170. 17. How large is the state space of StarCraft estimated to be? 1047 , 10170 , or 101685 ? Answer: The state space of StarCraft is estimated to be 101685. 18. What does the rainbow in the Rainbow paper stand for, and what is the main message? Answer: The ”Rainbow” stands for combining several improvements to the DQN algorithm. The main message is that integrating multiple enhancements can significantly boost perfor- mance in reinforcement learning. 19. Mention three Rainbow improvements that are added to DQN. Answer: Three Rainbow improvements are Double Q-learning, Prioritized Experience Re- play, and Dueling Network Architectures. 5 8 In Class Questions 1. What is Deep Reinforcement Learning? Answer: Deep Reinforcement Learning combines reinforcement learning (RL) and deep learning (DL), using deep neural networks to approximate value functions or policies in high- dimensional state and action spaces. 2. What is the Curse of Dimensionality? Answer: The Curse of Dimensionality refers to the exponential increase in computational complexity and data requirements as the number of dimensions (features) in the input space grows, making it difficult to learn and generalize. 3. What is ALE? Answer: ALE (Arcade Learning Environment) is a platform used for evaluating the perfor- mance of reinforcement learning algorithms using Atari 2600 games. 4. What is End-to-end in DRL for Atari? Answer: End-to-end in DRL for Atari means training a deep neural network directly from raw pixel inputs to game actions without manually designing features. 5. What is the biggest challenge in DRL for Atari? Answer: The biggest challenge in DRL for Atari is handling the high-dimensional input space and learning effective policies from raw pixel data. 6. What is the deadly triad? Answer: The deadly triad in reinforcement learning refers to the combination of function approximation, bootstrapping, and off-policy learning, which can lead to instability and di- vergence. 7. What is the Convergence problem in DRL? Answer: The convergence problem in DRL refers to the difficulty in ensuring that learning algorithms converge to an optimal policy, especially in the presence of the deadly triad and unstable training dynamics. 8. How does DQN solve the stability problem? Answer: DQN solves the stability problem using techniques like experience replay and target networks to break correlations in the data and provide stable target values. 9. What is Rainbow? Name three approaches. Answer: Rainbow is an integrated approach combining several improvements to DQN. Three approaches included in Rainbow are Double Q-learning, Prioritized Experience Replay, and Dueling Network Architectures. 10. What is Mujoco? Answer: Mujoco (Multi-Joint dynamics with Contact) is a physics engine used for simulating complex robotic and biomechanical systems, often used in reinforcement learning research. 11. What are the Stable Baselines? 6  Answer: Stable Baselines are a set of reliable implementations of reinforcement learning algorithms in Python, designed to provide stable and efficient learning. 12. Gym = X and Stable Baselines = Y Answer: Gym = Environments for training and testing RL agents, Stable Baselines = Implementations of RL algorithms. 13. What is the Zoo? Answer: The Zoo typically refers to a collection or repository of pre-trained reinforcement learning models or a suite of environments and benchmarks for evaluating RL algorithms. 7