Reinforcement Learning (RL) is a dynamic framework within machine learning where an agent learns to make a sequence of decisions by interacting with an environment to maximize cumulative rewards.
Central to RL are the concepts of the agent, environment, states, actions, and rewards, which create a feedback loop enabling the agent to learn optimal behaviors over time.
This foundational learning process is characterized by balancing two critical aspects: exploration, where the agent tries new actions to discover their effects, and exploitation, where it capitalizes on known actions that yield high rewards.
Foundations of Reinforcement Learning
Reinforcement Learning involves an agent operating within an environment that can be modeled as a Markov Decision Process (MDP). Here:
1. Agent: The learner or decision-maker that takes actions based on a policy.
2. Environment: The external system or world with which the agent interacts.
3. State: A representation of the current situation of the environment.
4. Action: A set of possible moves the agent can make.
5. Reward: A scalar feedback signal indicating the quality or desirability of an action taken.
The policy is the agent's strategy for choosing actions based on the current state, aiming to maximize the expected cumulative reward over time, often discounted to prioritize immediate rewards.
Reward Structures
Rewards guide the agent by assigning values to actions and their outcomes. They define the problem's goal and are typically structured as:
Reward design significantly influences the learning efficiency and success of RL algorithms. A well-defined reward encourages desired behaviors, while poorly designed rewards might lead to unintended actions.
Exploration vs. Exploitation Trade-Off
In RL, the agent must balance:
Exploration: Trying new or less-known actions to discover potentially better rewards. This is crucial in unknown or changing environments to avoid premature convergence to suboptimal policies.
Exploitation: Using current knowledge to maximize immediate reward based on the best-known actions.
This trade-off is central because excessive exploration may waste resources, while premature exploitation can trap the agent in local optima. Strategies to manage this balance include:
1. ε-greedy: Randomly choosing actions with a small probability ε and exploiting otherwise.
2. Softmax action selection: Probabilistically choosing actions based on estimated state-action values.
3. Upper Confidence Bound (UCB): Selecting actions based on optimism in the face of uncertainty.
Interaction Cycle in Reinforcement Learning
1. The agent observes the current state 
2. Choose an action 
π
3. The environment responds with a new state 
4. The agent updates its policy based on the reward and transitions
Over multiple iterations, the agent learns the optimal policy that maximizes expected cumulative rewards.