Lecture 8: Integrating Learning and Planning | Highlights and Annotations by Gistr.

Model-based RL learns an environment model for planning, contrasting with model-free methods. Combining model-free and model-based approaches (e.g., Dyna-Q, MCTS) improves efficiency by leveraging both real and simulated experiences. Simulation-based search, like MCTS, excels in planning, demonstrating superior performance in games. This segment provides a concise definition and explanation of model-based reinforcement learning. It details the cyclical process involving real-world experience, model building, planning using the model, and acting in the real world based on the plan. The advantages and disadvantages of this approach are also briefly touched upon.This segment explores the advantages of model-based reinforcement learning, particularly in scenarios where directly learning value functions or policies is challenging. The example of chess is used to illustrate how a model (the rules of the game) can be simpler to learn than a value function, enabling efficient planning through methods like tree search. This segment effectively contrasts model-free and model-based reinforcement learning methods. It highlights how model-free methods learn policies or value functions directly from experience, while model-based methods learn a model of the environment first, then use it for planning and improved decision-making. This segment details how model learning in reinforcement learning can be framed as a supervised learning problem. The speaker explains how experiences (state, action, reward, next state) from trajectories are transformed into training data for separate regression (reward prediction) and density estimation (next state distribution) tasks. Different loss functions (e.g., mean squared error, KL divergence) are suggested for these tasks, emphasizing the applicability of various supervised learning techniques to model learning. This segment introduces the table lookup model as a simple, albeit non-scalable, approach to model learning. It explains how this model estimates reward and next state probabilities based on empirical counts from observed transitions. The speaker clarifies that this method involves counting transitions and averaging rewards for each state-action pair, providing an intuitive understanding of the process without delving into complex mathematical equations. This segment contrasts learning a reward function with learning a value function in the context of chess. It explains that learning a reward function is simpler, requiring only the classification of terminal states (win, lose, draw), while learning a value function necessitates assessing the likelihood of winning from any given game state, a significantly more complex task. The speaker highlights how planning aids in value function learning, especially in tactical games like chess, where predicting outcomes without lookahead is challenging. This segment introduces sample-based planning, a method that leverages a learned model to generate sample trajectories. It contrasts this approach with dynamic programming, highlighting its efficiency in complex domains by focusing on high-probability events. The speaker explains how model-free reinforcement learning algorithms can then be applied to these sampled trajectories, making it a powerful technique for overcoming the curse of dimensionality in reinforcement learning. The speaker illustrates a model-free reinforcement learning algorithm using a simple example, starting with real-world experience, building a model, sampling experiences from that model, and finally, applying model-free reinforcement learning to estimate the value function. The process highlights how the model-free approach uses sampled experiences to estimate values, which differ from those obtained directly from real experience, but asymptotically converge to the correct answer with more data. This segment addresses the important aspect of exploration-exploitation in the context of model-based reinforcement learning. The speaker clarifies that the model-based approach itself doesn't inherently address exploration, and that exploration strategies remain necessary to ensure that the model adequately understands the less-explored parts of the environment. The need for exploration is emphasized despite the use of a model to guide actions. The speaker tackles a critical question regarding the balance between time spent generating a model and computing with it. The explanation emphasizes that real-world experience is often a premium, advocating for using all available real experience to create the best model. The process is described as an anytime procedure, where planning continuously happens alongside acting, with each occurring at its natural rate.This segment addresses the issue of uncertainty in the model due to uneven experience sampling. It introduces Bayesian model-based reinforcement learning as a method to account for this uncertainty by combining prior expectations with data. The speaker acknowledges the computational cost associated with this approach, contrasting it with the simpler maximum likelihood model used in the previous example. The discussion focuses on the performance limitations of model-based reinforcement learning when dealing with inaccurate models. The speaker clarifies that the performance is limited by the optimal policy of the learned model, which may differ from the real world's optimal policy. Alternative strategies, such as using model-free reinforcement learning when the model is unreliable or explicitly considering model uncertainty using Bayesian approaches, are suggested. This segment analyzes how different reinforcement learning algorithms, specifically Dyna-Q and a direct model-free approach, handle changes in the environment. It demonstrates Dyna-Q's superior adaptability due to its ability to learn from imagined transitions, allowing it to more efficiently avoid negative states and adapt to unexpected changes, unlike model-free methods which require significantly more data. This segment introduces the concept of forward search in model-based reinforcement learning, contrasting it with exploring the entire state space. It highlights the efficiency of focusing on the current state and its immediate future, solving a sub-Markov Decision Process (MDP) rather than the entire MDP, thus saving computational resources and improving efficiency. This segment provides a concise introduction to the game of Go, highlighting its historical significance and complexity, making it a compelling challenge for AI research. It sets the stage for the discussion of Monte Carlo Tree Search (MCTS) as a solution to this complex problem. This segment clearly explains the core concept of Monte Carlo evaluation in the context of Go, transitioning smoothly into the introduction of Monte Carlo Tree Search (MCTS) as a more sophisticated approach. It bridges the gap between simple evaluation and the tree-based search method. This segment details Monte Carlo Tree Search (MCTS), a sophisticated search method that builds a search tree by generating trajectories of experience and evaluating state-action pairs. It explains how MCTS improves its simulation policy iteratively, using the information gathered in the search tree to make better decisions and achieve strong performance in complex scenarios, illustrated by its application in the game of Go. This segment introduces TD search, an alternative to MCTS that leverages temporal difference learning for more efficient value estimation. It contrasts TD search with MCTS, highlighting the benefits of bootstrapping and its potential for improved performance in certain scenarios. This segment summarizes the key advantages of MCTS, emphasizing its efficiency, scalability, and ability to handle complex domains. The segment then showcases the algorithm’s remarkable impact on the progress of Go-playing AI, providing compelling evidence of its effectiveness. This segment details a comparative analysis of different reinforcement learning approaches in the context of a Go-playing AI. It showcases the superior performance of a hybrid method combining temporal difference learning with simulated experience (using Monte Carlo Tree Search) compared to using temporal difference learning on real experience alone or Monte Carlo Tree Search alone. The speaker highlights the synergy between learning from real-world trajectories and simulated experiences, leading to significantly improved winning rates against a benchmark program. The results demonstrate the effectiveness of bootstrapping and the benefits of integrating both general knowledge and situation-specific knowledge for optimal decision-making.