Beginner Explanation
Imagine you have a pet dog. Every time it sits when you ask, you give it a treat. If it barks instead, you ignore it. Over time, your dog learns that sitting gets it a reward, while barking does not. Reinforcement Learning works similarly: an agent (like your dog) learns to make good choices by trying different actions in an environment and receiving rewards (treats) or penalties (no treats). The goal is to figure out the best actions to take to get the most rewards over time.Technical Explanation
Reinforcement Learning (RL) involves an agent interacting with an environment to maximize cumulative rewards. The agent observes the current state of the environment, selects an action based on a policy, and receives feedback in the form of rewards. The core elements of RL are states, actions, rewards, and policies. A common algorithm is Q-learning, which updates the action-value function Q(s, a) based on the Bellman equation: Q(s, a) ← Q(s, a) + α[r + γ max_a’ Q(s’, a’) – Q(s, a)], where α is the learning rate, γ is the discount factor, r is the reward, s is the current state, and s’ is the next state. Here’s a simple implementation in Python using Q-learning: “`python import numpy as np # Initialize parameters alpha = 0.1 # Learning rate gamma = 0.9 # Discount factor num_actions = 4 # Number of possible actions num_states = 10 # Number of possible states Q = np.zeros((num_states, num_actions)) # Q-table # Example update state = 0 action = 1 reward = 1 next_state = 1 Q[state, action] += alpha * (reward + gamma * np.max(Q[next_state]) – Q[state, action]) “`Academic Context
Reinforcement Learning (RL) is grounded in the fields of control theory and behavioral psychology. It is defined mathematically as a Markov Decision Process (MDP), where the goal is to find a policy that maximizes the expected cumulative reward. Key concepts include exploration vs. exploitation, temporal difference learning, and policy gradients. Notable papers include “Playing Atari with Deep Reinforcement Learning” by Mnih et al. (2013), which introduced the Deep Q-Network (DQN), and “Proximal Policy Optimization Algorithms” by Schulman et al. (2017), which proposed a more stable policy optimization method. The theoretical foundation is often derived from Bellman’s equations and dynamic programming.Code Examples
Example 1:
import numpy as np
# Initialize parameters
alpha = 0.1 # Learning rate
gamma = 0.9 # Discount factor
num_actions = 4 # Number of possible actions
num_states = 10 # Number of possible states
Q = np.zeros((num_states, num_actions)) # Q-table
# Example update
state = 0
action = 1
reward = 1
next_state = 1
Q[state, action] += alpha * (reward + gamma * np.max(Q[next_state]) - Q[state, action])Example 2:
Reinforcement Learning (RL) involves an agent interacting with an environment to maximize cumulative rewards. The agent observes the current state of the environment, selects an action based on a policy, and receives feedback in the form of rewards. The core elements of RL are states, actions, rewards, and policies. A common algorithm is Q-learning, which updates the action-value function Q(s, a) based on the Bellman equation: Q(s, a) ← Q(s, a) + α[r + γ max_a' Q(s', a') - Q(s, a)], where α is the learning rate, γ is the discount factor, r is the reward, s is the current state, and s' is the next state. Here's a simple implementation in Python using Q-learning:
```python
import numpy as np
Example 3:
import numpy as np
# Initialize parameters
alpha = 0.1 # Learning rate
gamma = 0.9 # Discount factorView Source: https://arxiv.org/abs/2511.16671v1