Reinforcement Learning

In RL control problems, most methods take value functions as the core learning object, improving the policy indirectly by estimating long-term returns. However, when the state or action space becomes continuous, or when the policy itself must remain stochastic, this approach becomes less direct. Policy gradient methods adopt a different perspective by treating the policy itself as the object of optimization, directly performing gradient ascent on the expected return.

In practical control problems, the state and action spaces are often high-dimensional, continuous, and noisy, which makes reinforcement learning algorithms based on tabular methods difficult to apply directly. Once function approximation is introduced, the two components that are conceptually well separated in theory of value evaluation and policy improvement become tightly intertwined, bringing with them challenges related to stability and variance. This article focuses on on-policy control methods under function approximation, with particular attention to Sarsa.

This chapter focuses on on-policy prediction with approximation, systematically organizing the learning objectives for value estimation under this setting, the feasible learning methods, and the solutions to which they actually converge. By contrasting Gradient Monte Carlo with Semi-Gradient TD(0), we will see the unavoidable trade-offs that arise between theoretically well-defined objectives and methods that are practically viable.

In reinforcement learning (RL), an agent often needs to learn an effective decision policy under conditions where real interactions with the environment are limited and costly. Relying solely on real experience is conceptually straightforward, but it is often constrained by poor data efficiency and slow learning speed. Conversely, relying entirely on planning with a model may introduce bias when the model is inaccurate. The Dyna architecture was proposed to strike a balance between these two extremes by integrating acting, learning, and planning within a single learning process.

In Reinforcement Learning (RL), Dynamic Programming (DP) offers the most complete and mathematically explicit solution framework. However, its reliance on a known environment model makes it difficult to apply directly to real-world settings. Monte Carlo (MC) methods, in contrast, learn from experience without requiring a model, but they must wait until the end of an entire episode before performing updates, resulting in relatively coarse learning granularity. Temporal Difference (TD) learning represents a compromise between these two approaches: it does not require a model, yet it can update value estimates incrementally after each interaction step.

In Dynamic Programming (DP), having a complete environment model is a prerequisite for exact computation. However, this assumption rarely holds in most real-world problems. Monte Carlo (MC) methods choose to forgo reliance on an explicit model and instead learn directly from complete experiences generated through interaction with the environment. By sampling and averaging episode returns, MC provides a practical pathway for estimating value functions grounded in actual experience.

In Reinforcement Learning (RL), Dynamic Programming (DP) is the earliest and most complete solution framework. Although DP is almost impossible to apply directly to practical high-dimensional or continuous environments, it reveals the mathematical foundations of all core concepts in modern RL. At a fundamental level, the convergence objectives and update rules of all RL algorithms are derived from the Bellman Equations and the Generalized Policy Iteration (GPI) framework used in DP.