Reinforcement learning is type of machine learning that work on basically feedback based learning method. in that based on feedback after that improves automatically performance. we can say that agent is able to learn how to behave in an environment by trial and error.
In reinforcement learning , the agent learn from a automatically using feedbacks without using a labeled data.
So, basically there is no labeled data , so the agent is working on the future based on their experience only. also agent talk with environment and obeserve the their continuous actions. the goal of agent is that learn from the past experience and give major positive output.
1.Agent( ) :- explore a situation and act upon it.
2. Environment( ) :- A situation which an agent is present or surrounded by.
3. Actions( ) :- Move which are taken by agent in a environment.
4. State( ) :- situation status return by the environment after take all actions by agnet.
5. Reward( ) :- A feedback returned to the agent form the environment by evaluate the all actions.
6. Policy( ) :- Policy is statergy which taken by agent and applied by agent.
7. Value( ) :- It is expected long-term retuned with the discount factor.
8. Q-value( ) :- It is mostly similar to the value, but it takes one additional parameter as a current action.
In the Reinforcement learning , the agent is not instructed about the environment and what actions need to be taken.
It’s based on trial and error process.
Agent takes the next step based on a previous feedback of the actions.
Value based :- Value-based reinforcement learning aims to find the best value function for a given policy. The value function tells the agent how much it expects to earn in the long term by starting from a particular state and following that policy.
Policy based :- Policy-based reinforcement learning is a type of reinforcement learning that aims to find the best policy for an agent to follow, without explicitly learning a value function. A policy is a function that maps from states to actions, and it tells the agent what action to take in any given state.
There are two main types of policies in policy-based reinforcement learning:
- Deterministic policies: These policies always produce the same action for a given state.
- Stochastic policies: These policies produce an action with a certain probability for each state.
Model based :- In this we create a model for an environment . from that model through agent explore a environment and improve a response.
There is no particular solution or algorithm for a this approaches beacuse model represntation is different for each of the environments.
- Reward Signal
- Value Function
- Model of the Environment
Policy :- In reinforcement learning, a policy is a way for the agent to decide what to do. It is a function that maps from states to actions.
two main type of policies in this :
Deterministic policies always tell the agent to do the same thing in a given state. Stochastic policies tell the agent to do different things with different probabilities, depending on the state.
Stochastic policies are often used in reinforcement learning because they allow the agent to try different things and learn from its mistakes.
For deterministic policy: a = π(s)
For stochastic policy: π(a | s) = P[At =a | St = s]
Reward Signal :- The goal of reinforcement learning is to train an agent to behave in a way that maximizes its long-term rewards. The agent learns to behave by interacting with the environment and receiving rewards for taking good actions.
The environment provides the agent with a reward signal, which is a number that indicates how good the agent’s current state is. The agent’s goal is to maximize the total amount of reward it receives over time.
The agent’s policy is a function that maps from states to actions. The policy tells the agent what action to take in each state. The agent learns to update its policy over time based on the rewards it receives.
If the agent takes an action that leads to a high reward, it will increase the probability of taking that action in the future. If the agent takes an action that leads to a low reward, it will decrease the probability of taking that action in the future.
Value Function :- The value function tells the agent how good it is to be in a particular state and take a particular action. It is a measure of the expected long-term reward that the agent will receive if it starts in that state and takes that action.
The value function is different from the reward signal in that the reward signal is immediate, while the value function takes into account future rewards as well.
The agent’s goal is to maximize its long-term reward, so it needs to learn to choose actions that lead to states with high values.
Model of the Environment :- The last element of reinforcement learning is the model. A model is a representation of the environment that the agent can use to predict how the environment will behave. For example, a model could be a map of a video game world, or a mathematical model of a physical system.
The agent can use the model to plan its actions. It can try different actions in the model to see what results they lead to. This allows the agent to learn which actions are likely to lead to high rewards in the real world, without having to experience those actions directly.
Model-based reinforcement learning is an approach that uses a model to plan and learn. Model-based reinforcement learning algorithms can learn faster than model-free reinforcement learning algorithms, because they can explore the environment more efficiently. However, model-based reinforcement learning algorithms are also more complex and can be difficult to implement.
Model-free reinforcement learning is an approach that does not use a model. Model-free reinforcement learning algorithms learn by interacting with the environment directly. Model-free reinforcement learning algorithms are simpler to implement than model-based reinforcement learning algorithms, but they can learn more slowly.
The Bellman equation is a mathematical equation that is used to solve dynamic programming problems. It is named after Richard Bellman, who first introduced it in the 1950s.
In reinforcement learning, the Bellman equation is used to calculate the value function,
V(s) = max_a [R(s, a) + γ * V(s’)]
- V(s) is the value of state s
- R(s, a) is the reward for taking action in state
- γ is the discount factor, which determines how much the agent values future rewards relative to immediate rewards
- V(s’) is the value of the next state, s’, that the agent will transition to after taking action a in state s.
there is two types of reinforcement learning,
1.Positive Reinforcement Learning :- Positive reinforcement learning is a type of reinforcement learning that rewards the agent for taking actions that lead to desired outcomes. This makes the agent more likely to take those actions again in the future.
2.Negative Reinforcement Learning :- Negative reinforcement learning is a type of reinforcement learning that rewards the agent for avoiding undesired outcomes. This makes the agent more likely to avoid those outcomes in the future.
- Q-learning :-
Q-learning is a without model reinforcement learning algorithm that learns the value of an action in a particular state.
it handle a problems with stochastic transitions and rewards without requiring adaptations.
Q-learning works by maintaining a Q-table, which is a table that maps from state-action pairs to values. The Q-value of a state-action pair represents the expected long-term reward that the agent can expect to receive if it takes that action in that state.
The Q-learning algorithm updates the Q-table as follows:
Q(s, a) = Q(s, a) + α * (R(s, a) + γ * max_a’ Q(s’, a’) — Q(s, a))
- α is the learning rate, which controls how fast the agent learns
- γ is the discount factor, which controls how much the agent values future rewards relative to immediate rewards
- R(s, a) is the reward that the agent receive for take an action in state
- Q(s’, a’) is the Q-value of the next state, s’, that the agent transitions to after taking action a in state s
The Q-learning algorithm works by iteratively updating the Q-table until the Q-values converge. Once the Q-values have converged, the agent can select the optimal action in any state by simply selecting the action with the highest Q-value.
What is ‘Q’ in Q-learning?
The Q stands for quality , which means it specifies the quality of an action taken by the agent.
A Q-table is a created while performe the Q-learning.
The table follows the state and action pair, and initializes the values to zero. After each action, the table got updated, and the q-value are store within the table.
The Reinforcement Learning agent uses this Q-table as a reference table to select the best action based on the q-values.
2. SARSA :-
SARSA (State-Action-Reward-State-Action) is a model-free Reinforcement Learning algorithm that learns the value of a state-action pair. It’s similar to Q-learning, but it different in how it updates the Q-table.
Q-learning updates the Q-value for the state-action pair that the agent just took, regardless of the next action that the agent selects. SARSA, on the other hand, updates the Q-value for the state-action pair that the agent takes next.
This difference in how the Q-table is updated can lead to different learning behaviors. Q-learning is more off-policy, meaning that it learns from the Q-values of other actions, even if those actions are not the ones that the agent would actually select. SARSA is more on-policy, meaning that it learns from the Q-values of the actions that the agent is actually selecting.
But the sarsa is a very powerful algorithm and it’s used in variety of tasks,like playing games,controlling,etc….
- Robotics :- for navigation purpose.
- Business :- for making a stratergies planning.
- Finance :- Reinforcement Learning is currently used in the finance sector for evaluating trading strategies.
- Manufacturing :- many manufacturing companies use reinforcement learning into robots to picks some good and put them into a container.
From above discussion we can understand major purpose of the Reinforcement learning like it’s behaviour how it will works without human intervention . how agent improve their performance based on the previous feedback . it is mainly used in artificial intelligence. but it’s not work on biig data amount , for that you can use other algorithms for that.The main issue with the Reinforcement Learning algorithm is that some of the parameters may affect the speed of your learning, such as you get a delayed feedback.