Introduction to Reinforcement Learning with OpenAI Gym.
作者：禅与计算机程序设计艺术
1.简介
Reinforcement learning (RL) is a machine learning paradigm where an agent learns optimal decision-making strategies through trial and error interactions with its environment. Over the past decade, it has emerged as a highly popular and rapidly evolving field, driven by its ability to enable agents to learn complex tasks directly from raw experience without requiring explicit programming. RL algorithms are engineered to maximize cumulative rewards by identifying optimal actions based on the cumulative feedback received during agent-environment interactions. While constructing and training RL agents demand specialized knowledge of the problem domain and proficiency in advanced reinforcement learning techniques, including value functions, policies, and the Q-learning algorithm, among others.
To assist developers in expediting their comprehension of constructing and training an RL agent utilizing the OpenAI Gym library, this article offers step-by-step guides on integrating Python programming with OpenAI Gym to implement a variety of RL algorithms. Within its pages, we will delve into five fundamental RL methodologies - Tabular Methods, Monte Carlo Methods, Temporal Difference Methods, Deep Q Networks (DQN), and Policy Gradients. Each algorithm's corresponding code will be provided, facilitating readers' ability to modify and adapt the code for their unique projects. Furthermore, we will explore upcoming research directions and unresolved challenges that warrant further investigation to advance the frontiers of Reinforcement Learning.
By the conclusion of this article, you should attain an intuitive grasp of what Reinforcement Learning entails and comprehend its substantial impact. Additionally, you will gain proficiency in implementing various RL algorithms through OpenAI Gym and cultivate practical AI development skills by experimenting with diverse environments and hyperparameters. In essence, this article can function as a comprehensive guide for anyone aiming to delve deeper into Reinforcement Learning and initiate the creation of autonomous agents capable of solving problems independently.
This article is targeted towards software developers, data analysts, machine learning experts, or student populations interested in exploring new areas of AI development.
We expect this article to offer valuable insights into your path to becoming a professional in AI and your contribution to the expanding field of Artificial Intelligence. Take your first steps!
2.基本概念术语说明
本节，我们将介绍Reinforcement Learning所涉及的一些基本概念和术语。我们假设读者熟悉马尔可夫决策过程的基本知识。若读者对此不熟悉，我们建议您在继续深入学习之前，先阅读马尔可夫决策过程的入门文章。
Agent
An agent is defined as an entity that engages in actions within an environment with the objective of maximizing cumulative rewards. Agents span a diverse range of entities, including robots, traders, and humans. Each agent interacts with its environment by selecting actions that influence the subsequent state of the environment and receive feedback based on these actions' outcomes. A key distinction between these categories lies in whether the agent relies solely on local information about its surroundings or accesses global observations. A fully deterministic agent consistently selects the same action given a fixed policy, whereas a stochastic agent chooses actions probabilistically at each time step according to its policy. This fundamental difference highlights the role of information access in shaping agent behavior.
Environment
The environment encapsulates all external elements surrounding the agent, such as the physical surroundings, objects, sensors, and effectors. The primary objective of the agent is to engage with its environment and attain a desired outcome. Depending on the environmental conditions, the agent may be assigned multiple tasks, including navigation challenges or facial recognition within image datasets. Some illustrative examples of environments include video games, robotic simulations, stock market forecasts, and autonomous vehicles.
State
A状态是环境当前状况的一种表示方式。它由变量组成，这些变量代表环境背景的不同方面。状态告知智能体其内部状态以及可能影响状态转移的动作。状态由观测变量集合𝒮和其子集S定义。
Action
An action represents an agent's decision to perform actions in response to its perceptual inputs. Actions regulate the motion, forces applied, and torques exerted by the agent on the environment. Different actions may lead to distinct subsequent outcomes, thereby altering the environment's state. An action can be characterized by the set of all possible actions \mathcal{A}, or equivalently, the action space \mathcal{A}(s), where s is a specific state. Depending on the type of agent and environment, there may be multiple valid actions capable of leading to the same outcome.
Reward
A reward signal represents a scalar value provided by the environment to the agent as a consequence of achieving specific objectives or actions. Following an action, the agent perceives the reward signal and incurs a penalty if it does not meet the expected reward threshold. Reward functions quantify the magnitude of rewards obtained when an agent transitions to a specific state following a particular action.
Value Function
价值函数决定了当前状态的长期效用。它为每个状态分配了一个数值分数，以衡量该状态对智能体的益处。智能体致力于寻找能够使其未来总预期奖励最大化的状态。价值函数通常通过从智能体生成的随机轨迹样本中进行自抽样来估计。所估计的每个状态的值反映了由于样本数量有限而导致的估计不确定性。
Policy
A strategy establishes a correspondence between state space and action space. It specifies the behavior of the agent's decision-making process within each state while ensuring compliance with the constraints set by the environment. Usually, policies express a distribution over possible action options rather than a single deterministic choice, thereby allowing the agent to consider exploring alternative strategies when appropriate.
Exploration vs Exploitation
Exploration involves choosing actions that may not guarantee the most optimal solution. Instead of solely concentrating on exploiting known pathways, the agent actively seeks out uncharted regions to discover potentially rewarding solutions. This balance between exploration and exploitation is influenced by the epsilon greedy exploration parameter, which determines the degree of randomness in the decision-making process. When epsilon approaches zero, the agent becomes increasingly confident in its current knowledge and prioritizes exploitation, whereas higher values of epsilon lead to greater exploration at the expense of potential immediate rewards. This dynamic interplay ensures that the agent neither becomes overly reliant on known strategies nor neglects the opportunity to discover innovative approaches that could yield higher returns. The epsilon greedy parameter effectively modulates the agent's risk tolerance, striking a balance between the exploration of new possibilities and the exploitation of established ones. By carefully tuning this parameter, the agent can optimize its performance across diverse problem domains.
Model-based vs Model-free Approach
A model-based approach represents the underlying mechanism of constructing an environmental model that captures both static and dynamic aspects of the system's behavior. By leveraging the model, the agent can efficiently compute the optimal policy without the need for an explicit environmental model. In contrast, a model-free approach implies that the agent must independently devise strategies to manage the environmental complexities. This approach employs sampling-based methods, such as Monte Carlo Tree Search (MCTS) or Dynamic Programming, to continuously generate sample experiences and update the policy based on the generated experiences.
3.核心算法原理和具体操作步骤及数学公式讲解
This section is dedicated to detailing the core algorithms employed in Reinforcement Learning and providing in-depth explanations. Each algorithm possesses distinct characteristics and attributes, necessitating individualized explanations. The emphasis will be placed on six fundamental algorithms that are frequently employed in the field of Reinforcement Learning.
Tabular-Based Techniques, Monte Carlo-Based Approaches, Temporal Difference-Based Learning Methods, Deep Q-Networks (DQN), and Policy Gradient Methods
Tabular Methods and Monte Carlo Methods fall under the classical RL paradigm, characterized by their absence of neural networks. While effective in simple environments with limited state spaces, these methods often encounter challenges such as high variance and slow convergence rates. To overcome these limitations, we will turn to more advanced models including Deep Q-Networks (DQN), Policy Gradient (PG), and Temporal Difference with Lambda (TDLambda).
3.1 Tabular Methods
在强化学习中，表格法是最基本的形式之一，它指的是直接计算状态-动作值函数的方法。这些方法通过索引（状态，动作）对存储Q值表。在每个迭代过程中，智能体根据Q表中的最大值选择一个动作，并更新与该动作相关的Q值。通过这种方式，智能体逐步通过调整基于观察到的转移而更新的Q值来收敛到最优策略。
Algorithmic Steps
- 初始化Q表为空表
- 重复迭代直至收敛
- 观察当前状态s
- 选取动作a，使其在Q[s,a]中达到最大值
- 执行动作a，观察奖励r和新状态s'
- 更新Q表：Q[s,a]被更新为(1-alpha)乘以Q[s,a]加上alpha乘以(r + gamma乘以max_{a'} Q[s',a'])
where alpha represents the learning rate, gamma denotes the discount factor, and Q represents the Q-function.
Mathematical Analysis
For large-scale state spaces and continuous action spaces, traditional tabular methods can be computationally intensive and prone to divergence. Analyzing the performance of traditional methods, we will now consider alternative approaches that will enable us to scale up to larger and more complex environments.
Optimality of Q-Learning
Typically used in tabular Q-learning, the optimal action a* for each state s is selected through the process of identifying the action associated with the highest Q-value Q[s,a] accumulated during training. However, this straightforward approach may not always be adequate, as the true optimal action could require understanding preferences that aren’t immediately obvious, which are learned through experience. To address these limitations, we propose two key changes designed to ensure our agent learns the true optimal policy when dealing with complex environments:
- Double Q-learning
Instead of training a single Q-function, two distinct Q-functions are developed. Among the two Q-functions, one is dedicated to selecting the optimal action, represented by argmax_a Q1(s,a). The other function is employed to estimate the temporal difference error and update the Q-table, as shown by Q2(s,a') := (1−α)(Q2(s,a')) + α(r+γ * Q1(s',argmax_a Q2(s',a'))). By employing two Q-functions, the system demonstrates enhanced stability and mitigates the risk of overfitting.
- Prioritized Experience Replay
- Allocates importance sampling weights to each transition sampled from the replay buffer.
- Balances the trade-off between highly relevant and irrelevant samples to enhance learning speed and stability.
Therefore, modern tabular methods demonstrate proficiency in managing extensive and continuous action spaces, yet they often fall short in capturing non-trivial dependencies among state variables or in delaying updates to accurately reflect real-world changes. Furthermore, the inherent computational overhead resulting from parallelization and distributed architectures often hinders their practical application in real-world scenarios. Despite these limitations, they provide valuable insights into the foundational principles of reinforcement learning and offer an intuitive understanding of how RL systems operate internally.