deep learning systems unit 16 study guides

Fundamentals of Reinforcement Learning

• Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment
• The agent receives rewards or penalties based on its actions, which it uses to update its decision-making policy
• RL is modeled as a Markov Decision Process (MDP) consisting of states, actions, rewards, and transition probabilities
• The goal of the agent is to maximize the cumulative reward over time, often discounted by a factor $\gamma$ to prioritize near-term rewards
• Key components of RL include the policy $\pi(a|s)$, which maps states to actions, and the value function $V(s)$ or $Q(s,a)$, which estimates the expected cumulative reward from a given state or state-action pair
  • The policy can be deterministic or stochastic, with the latter allowing for exploration of the environment
  • The value function is used to evaluate the quality of a policy and guide the learning process
• RL algorithms can be classified as model-based or model-free, depending on whether they learn an explicit model of the environment or directly learn the policy or value function
• Examples of classic RL algorithms include Q-learning, SARSA, and policy gradient methods

Introduction to Deep Reinforcement Learning

• Deep reinforcement learning (DRL) combines RL with deep neural networks (DNNs) to enable learning in complex, high-dimensional environments
• DNNs are used to approximate the policy, value function, or both, allowing for efficient representation learning and generalization
• DRL has achieved remarkable success in various domains, such as playing video games (Atari), board games (Go, chess), and robotic control
• Key advantages of DRL include the ability to handle large state and action spaces, learn directly from raw sensory inputs (images, audio), and discover intricate strategies through trial and error
• Challenges in DRL include sample inefficiency, instability during training, and the difficulty of exploration in sparse reward environments
• DRL algorithms can be categorized into value-based methods (DQN), policy-based methods (REINFORCE), and actor-critic methods (A3C) that combine both approaches
• Value-based methods learn a value function and derive a policy from it, while policy-based methods directly learn a parameterized policy
• Actor-critic methods learn both a policy (actor) and a value function (critic) simultaneously, using the critic to guide the actor's updates

Key Algorithms in Deep RL

• Deep Q-Networks (DQN) extend the classic Q-learning algorithm to use DNNs for approximating the Q-function
  • DQN introduced techniques like experience replay and target networks to stabilize training and improve sample efficiency
  • Variants of DQN include Double DQN, Dueling DQN, and Prioritized Experience Replay
• Policy Gradient methods directly optimize the policy parameters using gradient ascent on the expected cumulative reward
  • REINFORCE is a simple policy gradient algorithm that estimates the gradient using Monte Carlo samples
  • Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are more advanced policy gradient methods that constrain the policy updates to ensure stability
• Actor-Critic methods combine value-based and policy-based approaches, using a critic network to estimate the value function and an actor network to learn the policy
  • Asynchronous Advantage Actor-Critic (A3C) and its synchronous variant (A2C) are popular actor-critic algorithms that enable parallel training
  • Soft Actor-Critic (SAC) is an off-policy actor-critic method that incorporates entropy regularization to encourage exploration
• Deterministic Policy Gradient (DPG) and Deep Deterministic Policy Gradient (DDPG) are algorithms designed for continuous action spaces, using a deterministic policy and a Q-function critic
• Distributional RL algorithms, such as C51 and QR-DQN, learn a distribution over returns instead of just the expected return, capturing the inherent uncertainty in the value estimates

Neural Network Architectures for Deep RL

• Convolutional Neural Networks (CNNs) are commonly used in DRL for processing visual inputs, such as images or video frames
  • CNNs consist of convolutional layers that learn local features, pooling layers for downsampling, and fully connected layers for decision-making
  • Examples of CNN architectures in DRL include the DQN architecture for Atari games and the ResNet architecture for board games like Go
• Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRUs), are used to handle sequential or temporal data in DRL
  • RNNs maintain a hidden state that captures the history of previous observations, allowing the agent to make decisions based on the context
  • RNNs are useful for tasks involving partially observable environments, such as text-based games or robotic control with sensory inputs
• Attention mechanisms, such as the Transformer architecture, have been applied to DRL to selectively focus on relevant parts of the input or memory
  • Attention allows the agent to attend to specific regions of an image, steps in a sequence, or elements in a set, enhancing its decision-making capabilities
• Graph Neural Networks (GNNs) have been used in DRL for tasks involving graph-structured data, such as molecular design or social network analysis
  • GNNs learn representations of nodes and edges in a graph, capturing the relationships and interactions between entities
• Hierarchical architectures, such as the Option-Critic or Feudal Networks, introduce multiple levels of abstraction in the decision-making process
  • Higher-level networks learn to select subgoals or options, while lower-level networks learn to execute primitive actions to achieve those subgoals
  • Hierarchical architectures can improve sample efficiency and generalization by decomposing complex tasks into simpler subtasks

Policy Optimization Techniques

• Policy optimization involves directly updating the parameters of the policy network to maximize the expected cumulative reward
• Gradient-based methods, such as REINFORCE and policy gradient, estimate the gradient of the expected reward with respect to the policy parameters
  • The gradient is typically estimated using Monte Carlo samples of the trajectory, which can be noisy and high-variance
  • Techniques like baseline subtraction and advantage estimation are used to reduce the variance of the gradient estimates
• Trust region methods, such as TRPO and PPO, constrain the policy updates to ensure stability and prevent large deviations from the previous policy
  • TRPO uses a second-order approximation of the KL divergence between the old and new policies to enforce a trust region constraint
  • PPO simplifies the trust region approach by using a clipped surrogate objective that penalizes large policy updates
• Natural gradient methods, such as Natural Policy Gradient (NPG) and Trust Region Policy Optimization with Natural Gradients (TRPO-NGA), use the Fisher information matrix to define a natural geometry for the policy parameter space
  • Natural gradient methods are invariant to reparameterization and can lead to faster convergence and better generalization
• Entropy regularization techniques, such as Soft Actor-Critic (SAC) and Maximum Entropy RL, encourage exploration by adding an entropy bonus to the reward function
  • Entropy regularization helps prevent premature convergence to suboptimal policies and promotes diversity in the learned behaviors
• Off-policy optimization methods, such as Q-Prop and ACER, leverage off-policy data to improve sample efficiency and stability
  • Off-policy methods can learn from a replay buffer of past experiences, allowing for more efficient use of the collected data
  • Importance sampling is used to correct for the distribution mismatch between the behavior policy and the target policy

Value Function Approximation

• Value function approximation involves estimating the expected cumulative reward from a given state or state-action pair using a parameterized function, typically a neural network
• The value function can be used to evaluate the quality of a policy, guide the learning process, or derive a policy directly (e.g., in value-based methods like Q-learning)
• Temporal Difference (TD) learning is a common approach for value function approximation, which updates the estimates based on the difference between the predicted and observed rewards
  • TD learning methods, such as SARSA and Q-learning, are model-free and can learn from incomplete episodes or off-policy data
  • TD($\lambda$) and Eligibility Traces are extensions of TD learning that incorporate a weighted average of n-step returns to balance between bias and variance
• Function approximation introduces challenges such as instability, divergence, and overestimation bias
  • Experience replay, target networks, and double Q-learning are techniques used to mitigate these issues in deep Q-learning
  • Gradient clipping, regularization, and careful hyperparameter tuning can also help stabilize the learning process
• Distributional RL methods, such as C51 and QR-DQN, learn a distribution over returns instead of just the expected return
  • Distributional methods capture the inherent uncertainty in the value estimates and have been shown to improve performance and robustness
• Dueling Network Architectures decompose the Q-function into separate value and advantage streams, which can lead to more stable and efficient learning
  • The value stream estimates the expected return from a given state, while the advantage stream captures the relative importance of each action
• Prioritized Experience Replay assigns higher sampling probabilities to transitions with larger TD errors, focusing the learning on the most informative experiences
  • Prioritized replay can significantly improve sample efficiency and convergence speed, especially in environments with sparse rewards

Exploration vs. Exploitation Strategies

• Exploration and exploitation are fundamental trade-offs in RL, where the agent must balance between gathering new information (exploration) and leveraging its current knowledge to maximize rewards (exploitation)
• $\epsilon$-greedy is a simple exploration strategy that selects a random action with probability $\epsilon$ and the greedy action (based on the current Q-values) with probability $1-\epsilon$
  • The value of $\epsilon$ is typically annealed over time to gradually shift from exploration to exploitation
  • Variants of $\epsilon$-greedy include decaying $\epsilon$, state-dependent $\epsilon$, and action-specific $\epsilon$
• Boltzmann exploration selects actions according to a softmax distribution over the Q-values, assigning higher probabilities to actions with higher estimated values
  • The temperature parameter $\tau$ controls the randomness of the action selection, with higher values favoring exploration and lower values favoring exploitation
  • Boltzmann exploration can be more effective than $\epsilon$-greedy in environments with multiple good actions or when the optimal action is not significantly better than the alternatives
• Upper Confidence Bound (UCB) algorithms, such as UCB1 and UCB-V, balance exploration and exploitation by selecting actions based on their estimated value and an exploration bonus that encourages trying less frequently visited state-action pairs
  • UCB algorithms have strong theoretical guarantees and have been applied successfully to multi-armed bandit problems and RL
• Bayesian exploration strategies, such as Thompson Sampling and Bayesian Q-learning, maintain a posterior distribution over the Q-values and sample from it to guide the action selection
  • Bayesian methods naturally balance exploration and exploitation by incorporating the uncertainty in the value estimates
  • Bayesian exploration can be more sample-efficient than traditional methods, especially in environments with complex reward structures or non-stationary dynamics
• Intrinsic motivation and curiosity-driven exploration aim to encourage the agent to seek novel or surprising experiences, rather than solely focusing on extrinsic rewards
  • Intrinsic rewards can be based on prediction errors, information gain, or state visitation counts, and are added to the extrinsic rewards to shape the agent's behavior
  • Examples of intrinsic motivation methods include Variational Information Maximizing Exploration (VIME), Random Network Distillation (RND), and Intrinsic Curiosity Module (ICM)

Advanced Topics and Current Research

• Hierarchical RL (HRL) aims to decompose complex tasks into simpler subtasks, allowing for more efficient learning and transfer across different environments
  • HRL methods, such as the Options Framework and Feudal Networks, introduce multiple levels of abstraction in the decision-making process
  • HRL can improve sample efficiency, generalization, and interpretability by learning reusable skills and subgoals
• Multi-agent RL (MARL) studies the interaction and coordination of multiple agents in a shared environment
  • MARL introduces challenges such as non-stationarity, credit assignment, and emergent behaviors
  • Examples of MARL algorithms include Independent Q-learning, Centralized Training with Decentralized Execution (CTDE), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
• Meta-RL and learning to learn aim to develop agents that can quickly adapt to new tasks or environments by learning a learning algorithm itself
  • Meta-RL methods, such as Model-Agnostic Meta-Learning (MAML) and RL$^2$, learn an initialization or update rule for the policy or value function that can be fine-tuned with a few gradient steps
  • Meta-RL has been applied to few-shot learning, task generalization, and continual learning scenarios
• Unsupervised RL and self-supervised learning aim to learn useful representations and skills without explicit reward signals
  • Unsupervised RL methods, such as Variational Intrinsic Control (VIC) and Diversity is All You Need (DIAYN), learn a diverse set of skills by maximizing the mutual information between states and skills
  • Self-supervised learning methods, such as Contrastive Predictive Coding (CPC) and Momentum Contrast (MoCo), learn representations by solving auxiliary tasks like predicting future states or distinguishing positive and negative examples
• Safe and robust RL aims to ensure that the learned policies are safe, reliable, and can handle uncertainties and perturbations in the environment
  • Safe RL methods, such as Constrained Policy Optimization (CPO) and Safe Exploration for Reinforcement Learning (SERL), incorporate safety constraints or risk measures into the optimization process
  • Robust RL methods, such as Robust Adversarial Reinforcement Learning (RARL) and Domain Randomization, aim to learn policies that are robust to variations in the environment dynamics or observation space
• Interpretable and explainable RL aims to develop methods that can provide insights into the decision-making process of the learned policies
  • Interpretable RL methods, such as Attention-based RL and Symbolic RL, use attention mechanisms or symbolic representations to highlight the relevant features or rules that influence the agent's behavior
  • Explainable RL methods, such as Causal RL and Counterfactual Explanations, aim to provide human-understandable explanations for the agent's decisions based on causal reasoning or counterfactual analysis