Reinforcement Learning Algorithms

RL algorithms are the techniques that allow agents to learn to make optimal decisions. Here we explore the main algorithms and their characteristics.

🧠 Q-Learning

Q-Learning is an algorithm that learns the Q-function, which estimates the value of taking an action in a given state.

Key Characteristics

• No model required of the environment
• Learns optimal policy directly
• Converges to optimal solution
• Perfect for discrete environments

How it Works

1. Initialize a Q-table with random values
2. For each interaction:
   • Observe current state
   • Choose an action (exploration/exploitation)
   • Receive reward and new state
   • Update Q(s,a) using Bellman equation

Bellman Equation

Q(s,a) = Q(s,a) + α[r + γ max Q(s',a') - Q(s,a)]

Where:

• α = learning rate
• γ = discount factor
• r = reward
• s' = next state

🎯 Deep Q-Network (DQN)

DQN extends Q-Learning to continuous state spaces using neural networks.

Key Characteristics

• Handles continuous states - No need for discretization
• Uses neural networks - Approximates Q-function
• Experience replay - Stores and reuses past experiences
• Target network - Stabilizes learning

Architecture

• Input layer - State representation
• Hidden layers - Feature extraction
• Output layer - Q-values for each action

🚀 Policy Gradient Methods

These algorithms learn the policy directly instead of value functions.

REINFORCE

• Direct policy learning - No value function needed
• Monte Carlo - Uses complete episodes
• High variance - Can be unstable

Actor-Critic

• Combines policy and value learning
• Lower variance - More stable than REINFORCE
• Faster learning - Uses value function estimates

🎮 Proximal Policy Optimization (PPO)

PPO is a modern policy gradient algorithm that's stable and efficient.

Key Characteristics

• Stable learning - Prevents large policy updates
• Sample efficient - Uses data multiple times
• Works well with continuous actions
• Popular in modern RL applications

📊 Algorithm Comparison

Algorithm	State Space	Action Space	Sample Efficiency	Stability
Q-Learning	Discrete	Discrete	High	High
DQN	Continuous	Discrete	Medium	Medium
REINFORCE	Any	Any	Low	Low
PPO	Any	Any	High	High

🎯 Choosing an Algorithm

For Beginners

• Q-Learning - Simple, easy to understand
• Good for discrete environments
• Start here before moving to deep RL

For Advanced Users

• DQN - For continuous states
• PPO - For complex environments
• Actor-Critic - For continuous actions

🔧 Implementation Tips

Q-Learning

• Start with small state/action spaces
• Tune learning rate carefully
• Monitor exploration rate

DQN

• Use experience replay - Essential for stability
• Update target network regularly
• Monitor loss - Should decrease over time

PPO

• Clip ratio - Usually 0.2
• Multiple epochs - 3-10 per update
• Monitor KL divergence - Prevent large updates

📚 Further Reading

• Q-Learning Tutorial
• Policy Gradient Methods
• PPO Algorithm
• RL Algorithm Zoo