Lecture 5: Model Free Control | Highlights and Annotations by Gistr.

Reinforcement learning's model-free control is explored, contrasting on-policy (e.g., SARSA) and off-policy (e.g., Q-learning) methods. Monte Carlo, TD learning, and their n-step/λ variations are detailed, addressing exploration-exploitation and the bias-variance tradeoff via techniques like ε-greedy and eligibility traces. Connections to dynamic programming are also made. This section delves into the limitations of using Monte Carlo policy evaluation with state value functions (V) for model-free control. The speaker explains why using V necessitates a model of the environment's dynamics for greedy policy improvement, highlighting the need for a model-free approach using action-value functions (Q). This segment highlights the practical significance of model-free control by showcasing its applicability to a wide range of real-world problems. Examples span diverse fields, from robotics and automation to finance and bioengineering, demonstrating the broad impact and relevance of the discussed techniques. This segment delves into the limitations of the Epsilon-greedy policy's proof, highlighting its inability to quantify exploration frequency. It emphasizes the need for an Epsilon decay schedule to ensure asymptotic convergence to the optimal policy while maintaining sufficient exploration. The discussion transitions into the integration of Monte Carlo methods for policy evaluation within the generalized policy iteration framework. This segment explains the crucial role of action-value functions (Q) in achieving model-free control. The speaker demonstrates how Q-values directly enable model-free policy improvement by eliminating the need for a model to predict the next state, allowing for direct action selection based on maximizing Q-values. The discussion also touches upon the exploration-exploitation dilemma inherent in model-free learning. This segment showcases the application of the GLIE Monte Carlo control algorithm to a Blackjack example. It presents the resulting optimal policy and value function, visually illustrating the algorithm's effectiveness in determining optimal behavior. The discussion addresses questions about specific policy decisions within the optimal strategy. This segment explains how the Sarsa algorithm integrates Temporal Difference (TD) learning into policy iteration, enabling more frequent policy updates at every time step. It contrasts this with Monte Carlo methods and highlights the advantages of online learning and using the freshest value function for action selection. The introduction of the Sarsa name and its visual representation in a decision node diagram enhances understanding. This segment explores optimizing Monte Carlo methods within the policy iteration framework. It discusses reducing the computational cost by updating the policy after each episode instead of waiting for full policy evaluation. The speaker explains the rationale behind this approach, emphasizing the use of the freshest value function estimates for improved policy updates. The discussion concludes with a question regarding guaranteeing the best possible policy.This segment introduces the Greedy in the Limit with Infinite Exploration (GLIE) concept, addressing the crucial balance between exploration and exploitation in reinforcement learning. It details the two conditions for GLIE: infinitely often visiting all states and actions, and asymptotic convergence to a greedy policy. The segment illustrates achieving this balance using an Epsilon-greedy policy with a decaying Epsilon schedule, such as a hyperbolic decay.This segment presents the GLIE Monte Carlo control algorithm, detailing its steps: sampling episodes, updating action values using an incremental mean update, and improving the policy using an Epsilon-greedy approach with a decaying Epsilon. The discussion highlights the algorithm's efficiency due to per-episode updates, contrasting it with batch-based methods. The speaker clarifies that the algorithm's convergence is unaffected by initial Q-value selection. This segment delves into the intuition behind the Sarsa update rule, connecting it to the Bellman equation. It clarifies the on-policy nature of Sarsa, where actions are selected and the policy is evaluated using the same policy. This explanation enhances understanding of why Sarsa's update rule effectively estimates the value of the current policy. This section provides a concise pseudocode representation of the Sarsa algorithm, clarifying its implementation details. It emphasizes the use of a Q-value lookup table, Epsilon-greedy policy for action selection (balancing exploration and exploitation), and the iterative update of Q-values based on the Sarsa update rule. The inclusion of a brief Q&A session addresses common questions about action selection in the algorithm. This segment presents a practical example using a Windy Gridworld environment to demonstrate Sarsa's application. It describes the environment's dynamics (wind affecting agent movement) and explains the optimal strategy for reaching the goal. The discussion of optimal behavior versus suboptimal strategies in the context of the windy gridworld strengthens the understanding of the algorithm's application. This segment explains the limitations of Sarsa(λ) as an offline algorithm and introduces the concept of eligibility traces to create an online, step-by-step learning algorithm. It details how eligibility traces assign credit or blame to state-action pairs based on their recency and frequency of occurrence in a trajectory, enabling immediate policy updates. This segment introduces n-step Sarsa and Q-returns, which bridge the gap between Monte Carlo and TD learning. It explains how n-step Sarsa uses n-step returns as targets for updating Q-values, providing a way to control the bias-variance tradeoff. The discussion of the Q-function's representation as a long-term estimate of total reward further clarifies its role in the algorithm. This segment clarifies several questions regarding the Sarsa(λ) algorithm, including the decay of eligibility traces, the online nature of updates, and how the algorithm handles multiple rewards within a single episode. The discussion provides further insights into the algorithm's mechanics and its advantages over Sarsa.This segment addresses a question about how Sarsa(λ) estimates the mean of value functions, explaining that it doesn't explicitly compute a mean but rather uses an incremental update approach based on bootstrapping. The discussion contrasts this method with Monte Carlo's explicit mean estimation and highlights the concept of a non-stationary mean in the context of bootstrapping. This section describes the initialization and step-by-step execution of the Sarsa(λ) algorithm. It emphasizes the role of eligibility traces in updating Q-values based on TD error and the impact of the lambda parameter on information propagation through time. The explanation is enhanced with a clear illustration of the algorithm's workings.This segment uses a grid world scenario to visually compare the update mechanisms of Sarsa and Sarsa(λ). It demonstrates how Sarsa(λ) propagates information backward through time more efficiently than Sarsa, highlighting the benefits of the lambda parameter in accelerating learning and reducing the dependency on the number of time steps. The impact of the lambda parameter on the bias-variance tradeoff is also discussed. This segment introduces Q-learning, a specific off-policy TD learning algorithm that avoids the high variance issues associated with importance sampling. It explains how Q-learning updates Q-values by considering both the actual action taken and an alternative action that would have been taken under the target policy, bootstrapping from the value of the alternative action to estimate the value under the target policy. The explanation includes a detailed comparison with other methods and its convergence to the optimal action-value function. This segment details the concept of learning from observation, specifically focusing on how to improve policy by observing human behavior or reusing data from previous policies. It introduces the challenge of evaluating the current policy while using data generated from various policies, highlighting the importance of off-policy learning to address this data inefficiency and improve value estimation.This segment explains how off-policy learning solves the exploration-exploitation dilemma in reinforcement learning. It emphasizes the use of an arbitrary exploration policy to effectively explore the state space while simultaneously learning the optimal policy, which inherently avoids exploration. The speaker highlights the necessity of off-policy methods to achieve this simultaneous learning process.