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In this tutorial, we will discuss the basic concepts of reinforcement learning and understand why it is so popular. Reinforcement learning offers powerful techniques so that training agents can make decisions in new environments. Reinforcement learning stands at the forefront of modern AI and has demonstrated its application from controlling robots to playing complex games like Alpha Go Better than a Champion.

What is reinforcement learning?

Reinforcement learning is a form of machine learning technique in which the agents are self-trained to make sequential decisions based on reward and punishment mechanisms. The agent interacts with the environment to reach the goal and aims to take the best possible path that gets maximum rewards with the least punishment. This reward and punishment mechanism acts as a signal for positive and negative behaviours. In this type of approach, the agent learns to complete a task through repeated trial-and-error interactions with a dynamic environment.

Core concepts in reinforcement learning:

Agent: It is the entity that makes decisions and takes actions while learning within an environment, and its goal is to maximize the rewards over time with the least punishments.

Environment: It is an external system where the agent learns and makes decisions. It is dynamic and responds to the actions taken by the agent to transition between different states and provide feedback to the agent.

State (s): It represents the current situation of the agent at a given time stamp and contains all relevant information required for decision-making by the agent.

Action (a): It is the list of actions or decisions made by an agent within the environment. An action can vary depending on the task at hand and can be discrete or continuous.

Reward (r):  It is usually a scalar value provided by the environment as feedback for the action performed by the agent, and this reward indicates the reward or punishment associated with the action that serves as a signal for learning for the agent.

Policy (π): It maps states to actions and represents the strategy or algorithm used by the agent to decide their actions. The goal of the agent here is to learn an optimal policy that maximizes the reward over time.

Value Function (V(s)) and Q-Value Function (Q(s, a)): This function estimates the expected cumulative reward of being in a particular state (V(s)) or taking a particular action in a particular state (q(s, a)). These functions evaluate the quality of different actions and guide the agent’s decision-making process.

Comparison with supervised and unsupervised learning

Aspect	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Training Data	Labeled	Unlabeled	Interaction with Environment
Feedback	Provided (labels)	Not provided	Provided by Environment (rewards/penalties)
Objective	Generalization to unseen data	Discover hidden patterns/structures	Goal-oriented, maximize cumulative rewards
Task Examples	Classification, Regression, and Object Detection	Clustering, Dimensionality Reduction, and Generative Modeling	Sequential Decision Making, Control Tasks
Decision Making Process	N/A	N/A	Sequential, Actions impact future states and reward

How Reinforcement learning works

• Inspiration: Reinforcement learning draws inspiration from behavioral psychology, focusing on how agents learn to make sequential decisions through interactions with an environment to achieve goals.
• Components: It involves an agent interacting with an environment. The agent observes the environment’s state, takes actions, and receives feedback (rewards) from the environment.
• Goal: The agent aims to learn a policy (mapping from states to actions) that maximizes cumulative rewards over time.
• Learning Process: The agent learns through trial and error, using learning algorithms like Q-learning or SARSA to update its policy based on received rewards.
• Balancing Exploration and Exploitation: The agent balances exploration (trying new actions) and exploitation (choosing known actions) to discover optimal strategies.
• Iterative Improvement: Through repeated interactions and learning, the agent gradually converges towards an optimal policy, maximizing cumulative rewards over time.

There are three major approaches to implement a reinforcement learning algorithm:

Value-based

• Objective: Maximize the value functionV(s).
• Meaning of V(s): It represents the total expected future reward an agent anticipates when starting from state 𝑠.
• Interpretation: V(s) tells us how valuable it is to be in a specific state 𝑠s.
• Policy-dependence: V^π(s) is the expected long-term return of state s under policy π, meaning it considers the strategy the agent follows (policy 𝜋) when making decisions.

Policy-based

• Objective: Design a policy to ensure that the actions taken by the agent in each state maximize future rewards.
• Policy Definition: The policy 𝜋π determines the next action to take at a given state 𝑠s, without involving a value function.
• Deterministic vs. stochastic methods:
  • Deterministic: The same action is consistently chosen by the policy at any given state.
  • Stochastic: Each action has a certain probability of being chosen, calculated using a specific equation.
• Focus: Emphasizes finding the best sequence of actions directly, without calculating the value of each state.

Model-based

• Objective: Develop a virtual model for each environment to enable the agent to learn optimal behavior within that specific environment.
• Approach: Unlike value-based and policy-based methods, which focus on direct interaction with the environment, model-based methods involve creating a representation (model) of the environment.
• Environment-specific Models: Each environment requires its own model, tailored to its dynamics and characteristics.
• No Universal Algorithm: Due to the variability of environments and their models, there’s no single solution or algorithm applicable across all scenarios.
• Learning from Models: The agent learns from these models, simulating interactions and planning actions based on the predicted outcomes within the virtual environment.

The Reinforcement Learning process

• Reinforcement Learning Process: Focuses on the interaction between agent and environment to learn decision-making and maximize rewards.
• Cycle: Agent observes state, selects action based on policy, receives reward, transitions to new state.
• Components:
  • Policy: The agent’s strategy for action selection, aiming for optimal decisions over time.
  • Value Functions: Estimate cumulative rewards in states. Includes State-value Function (V(s)) and action-value Function (Q(s, a)).
  • Bellman Equation: Fundamental relationship expressing values of states and successors. Used for value function calculations.

• Bellman Equations:
  • State-value Function
  • Action-value Function
• Importance: Understanding these components helps RL agents effectively learn and make optimal decisions in uncertain environments to maximize cumulative rewards.

Bellman Equation

State-value Function (V(s)):

• Definition: Estimates expected cumulative rewards from a given state under a specific policy.
• Bellman Equation: V(s)=E[R+γ⋅V(s′)∣s,a]
• Explanation: Calculates expected rewards by considering immediate reward (R) upon taking action (a) from state (s), along with the discounted value of the successor state (s’).

Action-value Function (Q(s, a)):

• Definition: Estimates expected cumulative rewards from taking a specific action (a) in a particular state (s).
• Bellman Equation: Q(s,a)=E[R+γ⋅max(Q(s′,a′))∣s,a]
• Explanation: Calculates expected rewards by considering immediate reward (R) upon taking action (a) from state (s), along with the maximum expected cumulative rewards over all possible actions in the successor state (s’).

Exploration vs. Exploitation

• Exploration:
  • Trying new actions and exploring unknown areas of the environment.
  • Aimed at gathering information and learning about potential favorable outcomes.
  • Particularly crucial in early learning stages when the agent’s knowledge is limited.
• Exploitation:
  • Leveraging current knowledge to select actions known to be effective.
  • Focuses on maximizing short-term rewards based on past experience.
  • There is a risk of missing potentially better actions if you solely rely on exploitation.
• Challenge:
  • Balancing exploration and exploitation is a key challenge.
  • The agent needs to explore to discover new strategies.
  • Simultaneously, it needs to exploit known actions to maximize cumulative rewards over time.

Real world applications in reinforcement learning

• Game Playing:
  • Success in chess, Go, and video games like AlphaGo and AlphaStar.
  • Achieved Grandmaster level in StarCraft II.
• Robotics:
  • Utilized for control, manipulation, navigation, and task planning.
  • Enables robots to learn motor skills, adapt to dynamic environments, and optimize behavior.
• Autonomous Vehicles:
  • Applied for navigation, lane following, path planning, and collision avoidance.
  • Enables vehicles to learn efficient driving behaviors in diverse environments.
• Recommendation Systems:
  • Personalizes content and optimizes user engagement.
  • Learns to recommend items adaptively based on user feedback and history.
• Finance and Trading:
  • Used for risk management, algorithmic trading, and portfolio management.
  • Learns to make optimal trading decisions by analyzing market data and predicting price movements.

Conclusion

In conclusion, in this blog, we learn about the basics of reinforcement learning algorithm. In the further blogs, we will be going to talk about reinforcement learning in detail.

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