A Practical Introduction to Deep Reinforcement Learning

TL;DR

The paper presents a concise, practice-focused introduction to deep reinforcement learning with a principal emphasis on the Proximal Policy Optimization (PPO) algorithm. It unifies existing DRL methods under the Generalized Policy Iteration (GPI) framework, emphasizing intuition, examples, and engineering practicality over rigorous proofs. Covering core components such as Monte Carlo and TD value estimation, policy gradients, Generalized Advantage Estimation (GAE), and off-policy corrections via importance sampling, the work culminates in a detailed PPO presentation with a clipped objective and entropy regularization. The aim is to equip readers with actionable knowledge and techniques to reliably implement and apply DRL methods to real-world problems.

Abstract

Deep reinforcement learning (DRL) has emerged as a powerful framework for solving sequential decision-making problems, achieving remarkable success in a wide range of applications, including game AI, autonomous driving, biomedicine, and large language models. However, the diversity of algorithms and the complexity of theoretical foundations often pose significant challenges for beginners seeking to enter the field. This tutorial aims to provide a concise, intuitive, and practical introduction to DRL, with a particular focus on the Proximal Policy Optimization (PPO) algorithm, which is one of the most widely used and effective DRL methods. To facilitate learning, we organize all algorithms under the Generalized Policy Iteration (GPI) framework, offering readers a unified and systematic perspective. Instead of lengthy theoretical proofs, we emphasize intuitive explanations, illustrative examples, and practical engineering techniques. This work serves as an efficient and accessible guide, helping readers rapidly progress from basic concepts to the implementation of advanced DRL algorithms.

Figures (8)

• Figure 1: Reinforcement learning has been extensively applied to a wide range of domains. (a) Game AI rl_applications_game_ai. (b) Recommendation systems rl_applications_recommendation_sys. (c) Autonomous driving rl_applications_autonomous_driving. (d) Biomedicine rl_applications_biomedicine. (e) Large language models (LLMs) rl_applications_llm. (f) Quantitative trading rl_applications_quantitative_trading. (g) Robotics rl_applications_robotics. (h) Computer animation rl_applications_computer_animation.
• Figure 2: The agent-environment interface. (a) A detailed representation of the frozen lake environment. Numbers in purple denote the states. The agent always starts at $s_0 = 0$. The episode ends when the agent reaches a hole ($s_t \in \{5, 7, 11, 12\}$) or the goal state ($s_t = 15$). In each non-terminal state, the agent can choose from four actions: move left, move down, move right, or move up, encoded as 0, 1, 2, and 3, respectively. (b) An illustration of the agent-environment interaction. At each time step $t$, the agent observes the state $\bm{s}_t$, selects an action $\bm{a}_t$ based on this observation, transitions to the next state $\bm{s}_{t+1}$, and receives a reward signal $r_{t+1}$.
• Figure 3: An illustration of the relationship between value functions. This figure depicts a scenario where the lake is slippery. The left panel illustrates a portion of the policy $\pi$ being evaluated. In the highlighted yellow block, the agent is in state 14 and can choose among three actions under policy $\pi$: move right, move left, or move up, with respective probabilities of 0.5, 0.25, and 0.25. The green, orange, and blue blocks represent segments of the potential trajectories following the agent's action. Due to the slipperiness of the lake, the agent may reach the intended next state (solid-line arrows) with a probability of 0.8 or one of two unintended states (dashed-line arrows) with a probability of 0.1 each. The grey block indicates that if the agent reaches the target state, the episode terminates with no future rewards. If the next state is not the target, subsequent actions are deterministic under the current policy $\pi$.
• Figure 4: Illustration of Generalized Policy Iteration. (a) A unified perspective on reinforcement learning algorithms. (b) An illustration of the truncation in the policy evaluation process. The blue arrows indicate the complete policy evaluation process, while the green arrows demonstrate the truncated policy evaluation process.
• Figure 5: Illustration of an iteration in GPI. We use the frozen-lake environment as an example. (a) The process begins with a random policy, where each action has an equal probability of being selected in all states. (b) State values are estimated for each state under the random policy using Monte Carlo methods. (c) During the policy improvement procedure, the action leading to the state with the highest state value is selected.