reinforcement

## What I do - Build reinforcement learning systems - Design reward functions and environments - Implement policy optimization algorithms - Train agents for sequential decision-making - Apply RL to games and robotics - Handle exploration-exploitation tradeoffs ## When to use me Use me when: - Building AI for games - Training robotics control systems - Optimizing resource allocation - Creating adaptive systems - Solving sequential decision problems ## Key Concepts ### RL Framework ``` Agent ──────▶ Action ──────▶ Environment │ │ │◀─── State + Reward ◀──│ │ │ └─────── (Loop) ─────────┘ Goal: Maximize cumulative reward ``` ### OpenAI Gym Example ```python import gym import numpy as np from collections import defaultdict # Create environment env = gym.make("CartPole-v1") state = env.reset() # Q-Learning Implementation class QLearningAgent: def __init__(self, n_actions, learning_rate=0.1, epsilon=0.1, gamma=0.99): self.q_table = defaultdict(lambda: np.zeros(n_actions)) self.lr = learning_rate self.epsilon = epsilon self.gamma = gamma self.n_actions = n_actions def choose_action(self, state): if np.random.random() < self.epsilon: return env.action_space.sample() return np.argmax(self.q_table[state]) def learn(self, state, action, reward, next_state): current_q = self.q_table[state][action] max_next_q = np.max(self.q_table[next_state]) new_q = current_q + self.lr * (reward + self.gamma * max_next_q - current_q) self.q_table[state][action] = new_q # Training loop agent = QLearningAgent(env.action_space.n) episodes = 1000 for episode in range(episodes): state = env.reset() done = False while not done: action = agent.choose_action(state) next_state, reward, done, _ = env.step(action) agent.learn(state, action, reward, next_state) state = next_state ``` ### Key Algorithms - **Q-Learning**: Value-based, off-policy - **SARSA**: On-policy value-based - **DQN**: Deep Q-Networks - **A2C/A3C**: Actor-Critic - **PPO**: Proximal Policy Optimization - **DDPG**: Continuous actions - **AlphaZero**: Tree search + RL ### Key Concepts - **Reward shaping**: Designing reward signals - **Exploration**: Epsilon-greedy, entropy - **Credit assignment**: Delayed rewards - **Function approximation**: Deep RL