Supervised learning, types of Reinforcement learning algorithms, and Unsupervised learning are significant areas of the Machine learning domain. Starting with the basic introduction of Reinforcement and its types, it’s all about exerting suitable decisions or actions to maximize the reward for an appropriate condition. Many software & machine models vigorously use it to get the best possible way or act in a particular situation.
The reinforcement learning algorithm/method, agent, or model, learns by having interactions with its environment; the agent obtains rewards by performing correctly & also gets penalties by performing incorrectly. The agent recognizes without having mediation with the human by making greater rewards & minimizing his penalties. The reinforcement learning algorithm and its types operate combinedly with the system of rewards & punishments.
Types of reinforcement learning algorithms: And How does RL Relate with Other ML Techniques?
Reinforcement Learning is a type of ML technique that enables an agent to learn in a competitive & interactive environment by trial & error using feedback from its actions & experiences.
Though both the Reinforcement & supervised learning methods use mapping between input & output, unlike supervised learning, where feedback provided to the agent is the correct set of actions for completing a task, reinforcement learning uses rewards & punishments as signals for positive & negative behavior.
As compared to unsupervised learning – reinforcement learning is quite different in terms of goals. While the goal in supervised learning is to find differences & similarities between data points, in reinforcement learning, the main goal is to find an appropriate action model that would maximize an agent’s total cumulative reward. The figure below shows the basic ideas & elements involved in the reinforcement learning model.
Types of reinforcement learning algorithms: How to Formulate a Basic Reinforcement Learning Problem?
Some of the key terms that best describe the elements of Reinforcement Learning problems are:
- Environment: Physical world in which an agent operates.
- State: It represents the current situation of the agent.
- Reward: Feedback from the environment.
- Value: It’s the future reward that an agent would receive by taking action in a particular state.
A Reinforcement Learning problem can be best described through games. Let’s take the example of the famous game “Pacman,” where the agent’s aim (which is Pacman) is to eat the food in the grid while avoiding the ghosts on its way.
The grid world is the interactive environment for the Pacman (agent). Pacman receives a reward for consuming food in the game & gets punishment if it gets killed by the ghost (loses the game). The states here are Pacman’s location in the grid world & the total cumulative reward is Pacman (agent) winning the game.
To build an optimal policy, the agent faces the dilemma of exploring new states while maximizing its reward simultaneously – This is known as Exploration VA Exploration trade-off.
Markov Decision Processes (MDPs) are some mathematical frameworks used to describe an environment in reinforcement learning, types of Reinforcement Learning algorithms & almost all Reinforcement Learning problems can be formalized using MDPs.
An MDP possesses a set of finite environment states S, a collection of possible actions A(s) in each state, a real-valued function R(S) & a transition model P(s ‘, s | a). However, real-life domain are more likely to lack any prior knowledge of environmental dynamics. Model-free Reinforcement Learning methods come in handy in such scenarios.
Q-learning is one of the most commonly used model-free approaches which can be used for building/making a self-playing agent (Pacman). It revolves around updating Q values, which denotes the value of doing action a in state s. The value update rule is the fundamental element of the Q-learning algorithm.
Types of reinforcement learning algorithms
Two types of Reinforcement Learning Algorithms or methods are:
Positive Reinforcement Learning
Positive reinforcement learning is defined as an event that occurs because of specific behavior. It increases the strength & the frequency of the behavior & positively impacts the action taken by the agent.
This type of Reinforcement learning (RL) algorithm or method helps you maximize the performance & sustain change for a more extended period. However, too much Reinforcement may cause over-optimization of the state, which can affect the results.
Negative Reinforcement Learning
This type of Reinforcement Learning algorithm is defined as strengthening behavior that occurs because of a negative condition that should have avoided or stopped. Negative Reinforcement Learning helps you to determine the minimum standard of performance. However, this algorithm or method’s drawback is that it provides enough to meet up the minimum behavior.
Approaches to Implement a Reinforcement Learning Algorithm
There are basically three approaches or ways to implement a Reinforcement Learning algorithm.
In a value-based Reinforcement Learning (RL) algorithm/method, you should try to maximize a value function V(s). In this method, the agent expects a long-term return of all the current states under policy ?.
In this type of Reinforcement Learning Algorithm/method, you try to develop such a policy that the action performed in every state helps you gain maximum reward in the future.
Two types of policy-based algorithms/methods are:
- Deterministic Method: For any state – the same action is produced by the policy ?.
- Stochastic Method: Every action has a distinct possibility, which is determined by the following equation Stochastic Policy:
In this type of Reinforcement Learning algorithm/method, you need to create a virtual model for each environment. The agent learns to perform specific tasks in that particular environment.
Characteristics & Applications of Reinforcement Learning Algorithms
When you need to understand which sort of situation needs an action when you want to explore – which action produces the maximum amount of rewards for an extended period, you probably need the reinforcement learning algorithms and methods.
Also, to get the learning agent and reward function & to estimate possible method or procedure when you want to get the largest reward, reinforcement learning algorithms play a vital role.
With this amount of specialties, types of reinforcement learning algorithms exhibit the following characteristics:
- Excluded with supervisor, & possess only a real number or reward signal.
- Appropriate decision making in sequential order.
- The reward for each & every type of action in reinforcement problems.
- Delayed feedback for actions
- To determine succeeding data by agent’s actions.
Reinforcement learning and types of reinforcement learning algorithms have numerous applications based on rewards or experience of actions:
- Machine Learning & data processing.
- Robotics for industrial automation.
- Creating training systems for custom instruction.
- Different aspects of materials for the requirement of students.
- Planning & making strategies for businesses.
- Controlling aircraft & robotic motion.
Why Use Reinforcement Learning?
Some main reasons for using Reinforcement Learning algorithms are:
- It helps you to discover which action field has the highest reward over the longer period.
- Reinforcement Learning helps you to find which situation needs an action.
- It also provides the learning agent with a reward function.
Challenges of Reinforcement Learning
One of the biggest challenges in reinforcement learning lies in preparing the simulation environment, which is highly dependent on the task to be performed. When the model has to go superhuman in Chess, Go, or Atari games, preparing the simulation environment is comparatively simple.
When it comes to a model capable of driving an autonomous car, building a realistic simulator is vital before letting the car ride on the street. The Reinforcement Learning model has to figure out how to brake or avoid a collision in a safe environment, where sacrificing even a hundred cars comes at a minimal cost.
Transferring the model out of the training environment & into the real world is where things get tricky. Scaling & tweaking the neural network controlling the agent is another big challenge. There’s no way to communicate with the network other than through the system of rewards & penalties.
Another big challenge is reaching a local optimum – that is, the agent performs the task as it is, but not in the required way. A “jumper” jumping like a kangaroo instead of doing what was anticipated of it-walking is a great example.
Types of reinforcement learning algorithms: Conclusion
In this post, we have tried to explain the Reinforcement Learning algorithm’s basic concept and its types. Today reinforcement has become a fantastic field to explore & learn. Many significant developments had been made in this field & many more yet to come in the coming future.
The important distinguishing factor of types of Reinforcement Learning algorithm is how the agent is trained. Instead of inspecting the data, the Reinforcement Learning (RL) model/method interacts with the environment, seeking ways to maximize the ‘reward.’ In the case of deep Reinforcement Learning, a neural network is in charge of storing the experiences & thus improves the way the task is performed.
Reinforcement Learning (RL) is, no doubt, cutting-edge technology that has a great potential to transform our world. However, this technology is needed to be used in every case. Nevertheless, Reinforcement learning seems to be the most likely way to make a machine more creative.