Reinforcement learning (RL) is defined as a sub-field of machine learning that enables AI-based systems to take actions in a dynamic environment through trial and error methods to maximize the collective rewards based on the feedback generated for respective actions. This article explains reinforcement learning, how it works, its algorithms, and some real-world uses.
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What Is Reinforcement Learning?
Reinforcement learning (RL) refers to a sub-field of machine learning that enables AI-based systems to take actions in a dynamic environment through trial and error to maximize the collective rewards based on the feedback generated for individual activities. In the RL context, feedback refers to a positive or negative notion reflected through rewards or punishments.
RL optimizes AI-driven systems by imitating natural intelligence that emulates human cognition. Such a learning approach helps computer agents make critical decisions that achieve astounding results in the intended tasks without the involvement of a human or the need for explicitly programming the AI systems.
Some known RL methods that have added a subtle dynamic element to conventional ML methods include Monte Carlo, state–action–reward–state–action (SARSA), and Q-learning. AI models trained over reinforcement learning algorithms have defeated human counterparts in several video games and board games, including chess and Go.
Technically, RL implementations can be classified into three types:
- Policy-based: This RL approach aims to maximize the system reward by employing deterministic policies, strategies, and techniques.
- Value-based: Value-based RL implementation intends to optimize the arbitrary value function involved in learning.
- Model-based: The model-based approach enables the creation of a virtual setting for a specific environment. Moreover, the participating system agents perform their tasks within these virtual specifications.
A typical reinforcement learning model can be represented by:
In the above figure, a computer may represent an agent in a particular state (St). It takes action (At) in an environment to achieve a specific goal. As a result of the performed task, the agent receives feedback as a reward or punishment (R).
Benefits of reinforcement learning
Reinforcement learning solves several complex problems that traditional ML algorithms fail to address. RL is known for its ability to perform tasks autonomously by exploring all the possibilities and pathways, thereby drawing similarities to artificial general intelligence (AGI).
The key benefits of RL are:
- Focuses on the long-term goal: Typical ML algorithms divide problems into subproblems and address them individually without concern for the main problem. However, RL is more about achieving the long-term goal without dividing the problem into sub-tasks, thereby maximizing the rewards.
- Easy data collection process: RL does not involve an independent data collection process. As the agent operates within the environment, training data is dynamically collected through the agent’s response and experience.
- Operates in an evolving & uncertain environment: RL techniques are built on an adaptive framework that learns with experience as the agent continues to interact with the environment. Moreover, with changing environmental constraints, RL algorithms tweak and adapt themselves to perform better.
How Does Reinforcement Learning Work?
The working principle of reinforcement learning is based on the reward function. Let’s understand the RL mechanism with the help of an example.
Let’s assume you intend to teach your pet (dog) certain tricks.
- As your pet cannot interpret our language, we need to adopt a different strategy.
- We design a situation where the pet performs a specific task and offer a reward (such as a treat) to the pet.
- Now, whenever the pet faces a similar situation, it tries to perform the same action that had previously earned him the reward with more enthusiasm.
- The pet thereby ‘learns’ from its rewarding experiences and repeats the actions as it now knows ‘what to do’ when a particular situation arises.
- On similar lines, the pet also becomes aware of the things to avoid if it encounters a specific situation.
In the above case,
- Your pet (dog) acts as an agent that moves around the house, which is the environment. Here, the state refers to the dog’s position of sitting, which can be changed to walking when you utter a particular word.
- The transition from sitting to walking occurs when the agent reacts to your word when in the environment. Here, the policy allows agents to take action in a particular state and expect a better outcome.
- After the pet transitions to a second state (walk), it gets a reward (dog food).
RL stepwise workflow
The reinforcement learning workflow involves training the agent while considering the following key factors:
Let’s understand each one in detail.
Step I: Define/Create the environment
The RL process begins by defining the environment in which the agent stays active. The environment may refer to an actual physical system or a simulated environment. Once the environment is determined, experimentation can begin for the RL process.
Step II: Specify the reward
In the next step, you need to define the reward for the agent. It acts as a performance metric for the agent and allows the agent to evaluate the task quality against its goals. Moreover, offering appropriate rewards to the agent may require a few iterations to finalize the right one for a specific action.
Step III: Define the agent
Once the environment and rewards are finalized, you can create the agent that specifies the policies involved, including the RL training algorithm. The process can include the following steps:
- Use appropriate neural networks or lookup tables to represent the policy
- Choose the suitable RL training algorithm
Step IV: Train/Validate the agent
Train and validate the agent to fine-tune the training policy. Also, focus on the reward structure RL design policy architecture and continue the training process. RL training is time-intensive and takes minutes to days based on the end application. Thus, for a complex set of applications, faster training is achieved by using a system architecture where several CPUs, GPUs, and computing systems run in parallel.
Step V: Implement the policy
Policy in the RL-enabled system serves as the decision-making component deployed using C, C++, or CUDA development code.
While implementing these policies, revisiting the initial stages of the RL workflow is sometimes essential in situations when optimal decisions or results are not achieved.
The factors mentioned below may need fine-tuning, followed by retraining of the agent:
- RL algorithm configuration
- Reward definition
- Action / state signal detection
- Environmental variables
- Training structure
- Policy framework
Reinforcement Learning Algorithms
RL algorithms are fundamentally divided into two types: model-based and model-free algorithms. Sub-dividing these further, algorithms fall under on-policy and off-policy types.
In a model-based algorithm, there exists a defined RL model that learns from the current state, actions, and state transitions occurring due to the actions. Thus, these types store state and action data for future reference. On the other hand, model-free algorithms operate on trial and error methods, thereby eliminating the need for storing state and action data in the memory.
On-policy and off-policy algorithms can be better understood with the help of the following mathematical notations:
The letter ‘s’ represents the state, the letter ‘a’ represents action, and the symbol ‘π’ represents the probability of determining the reward. Q(s, a) function is helpful for the prediction process and offers future rewards to the agents by comprehending and learning from states, actions, and state transitions.
Thus, on-policy uses the Q(s, a) function to learn from current states and actions, while off-policy focuses on learning [Q(s, a)] from random states and actions.
Moreover, the Markov decision process emphasizes the current state, which helps predict future states rather than relying on past state information. This implies that the future state probability depends on current states more than the process that leads to the current state. Markov property has a crucial role to play in reinforcement learning.
Let’s now dive into the vital RL algorithms:
Q-learning is an off-policy and model-free type algorithm that learns from random actions (greedy policy). ‘Q’ in Q-learning refers to the quality of activities that maximize the rewards generated through the algorithmic process.
The Q-learning algorithm uses a reward matrix to store the earned rewards. For example, for reward 50, a reward matrix is constructed that assigns a value at position 50 to denote reward 50. These values are updated using methods such as policy iteration and value iteration.
Policy iteration refers to policy improvement or refinement through actions that amplify the value function. In a value iteration, the values of the value function are updated. Mathematically, Q-learning is represented by the formula:
Q(s,a) = (1-α).Q(s,a) + α.(R + γ.max(Q(S2,a)).
alpha = learning rate,
gamma = discount factor,
R = reward,
S2 = next state.
Q(S2,a) = future value.
The State-Action-Reward-State-Action (SARSA) algorithm is an on-policy method. Thus, it does not abide by the greedy approach of Q-learning. Instead, SARSA learns from the current state and actions for implementing the RL process.
3. Deep Q-network (DQN)
Unlike Q-learning and SARSA, deep Q-network uses a neural network and does not depend on 2D arrays. Q-learning algorithms are inefficient in predicting and updating the state values they are unaware of, generally unknown states.
Hence, in DQN, 2D arrays are replaced by neural networks for the efficient calculation of state values and values representing state transitions, thereby speeding up the learning aspect of RL.
Uses of Reinforcement Learning
Reinforcement learning is designed to maximize the rewards earned by the agents while they accomplish a specific task. RL is beneficial for several real-life scenarios and applications, including autonomous cars, robotics, surgeons, and even AI bots.
Listed here are the critical uses of reinforcement learning in our day-to-day lives that shape the field of AI.
1. Managing self-driving cars
For vehicles to operate autonomously in an urban environment, they need substantial support from the ML models that simulate all the possible scenarios or scenes that the vehicle may encounter. RL comes to the rescue in such cases as these models are trained in a dynamic environment, wherein all the possible pathways are studied and sorted through the learning process.
Learning from experience makes RL the best choice for self-driving cars that need to make optimal decisions on the fly. Several variables, such as managing driving zones, handling traffic, monitoring vehicle speeds, and controlling accidents, are handled well through RL methods.
A team of researchers has developed one such simulation for autonomous units such as drones and cars at MIT, which is named ‘DeepTraffic’. The project is an open-source environment that develops algorithms by combining RL, deep learning, and computer vision constraints.
2. Addressing the energy consumption problem
With the meteoric rise in AI development, administrations can handle grave problems such as energy consumption today. Moreover, the rising number of IoT devices and commercial, industrial, and corporate systems have kept servers on their toes.
As reinforcement learning algorithms gain popularity, it has been identified that RL agents without any prior knowledge of server conditions have been capable of controlling the physical parameters surrounding the servers. The data for this is acquired through multiple sensors that collect temperature, power, and other data, which helps the training of deep neural networks, thereby contributing to the cooling of data centers and regulating energy consumption. Typically, Q-learning network (DQN) algorithms are used in such cases.
3. Traffic signal control
Urbanization and the rising demand for vehicles in metropolitan cities have raised the alarm for authorities as they struggle to manage traffic congestion in urban environments. A solution to this issue is reinforcement learning, as RL models introduce traffic light control based on the traffic status within a locality.
This implies that the model considers the traffic from multiple directions and then learns, adapts, and adjusts traffic light signals in urban traffic networks.
RL plays a vital role in the healthcare sector as DTRs (Dynamic Treatment Regimes) have supported medical professionals in handling patients’ health. DTRs use a sequence of decisions to come up with a final solution. This sequential process may involve the following steps:
- Determine the patient’s live status
- Decide the treatment type
- Discover the appropriate medication dosage based on the patient’s state
- Decide dosage timings, and so on
With this sequence of decisions, doctors can fine-tune their treatment strategy and diagnose complex diseases such as mental fatigue, diabetes, cancer, etc. Moreover, DTRs can further help in offering treatments at the right time, without any complications arising due to delayed actions.
Robotics is a field that trains a robot to mimic human behavior as it performs a task. However, today’s robots do not seem to have moral, social, or common sense while accomplishing a goal. In such cases, AI sub-fields such as deep learning and RL can be blended (Deep Reinforcement Learning) to get better results.
Deep RL is crucial for robots that help in warehouse navigation while supplying essential product parts, product packaging, product assembly, defect inspection, etc. For example, deep RL models are trained on multimodal data that are key to identifying missing parts, cracks, scratches, or overall damage to machines in warehouses by scanning images with billions of data points.
Moreover, deep RL also helps in inventory management as the agents are trained to localize empty containers and restock them immediately.
RL helps organizations maximize customer growth and streamline business strategies to achieve long-term goals. In the marketing arena, RL aids in making personalized recommendations to users by predicting their choices, reactions, and behavior toward specific products or services.
RL-trained bots also consider variables, such as evolving customer mindset, which dynamically learns changing user requirements based on their behavior. It allows businesses to offer targeted and quality recommendations, which, in turn, maximizes their profit margins.
Reinforcement learning agents learn and adapt to the gaming environment as they continue to apply logic through their experiences and achieve the desired results by performing a sequence of steps.
For example, Google’s DeepMind-created AlphaGo outperformed the master Go player in Oct. 2015. It was a gigantic step for the AI models of the time. Besides designing games such as AlphaGo that use deep neural networks, RL agents are employed for game testing and bug detection within the gaming environment. Potential bugs are easily identified as RL runs multiple iterations without external intervention. For example, gaming companies such as Ubisoft use RL to detect bugs.
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Reinforcement learning automates the decision-making and learning process. RL agents are known to learn from their environments and experiences without having to rely on direct supervision or human intervention.
Reinforcement learning is a crucial subset of AI and ML. It is typically helpful for developing autonomous robots, drones, or even simulators, as it emulates human-like learning processes to comprehend its surroundings.
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