reinforcement
Reinforcement learning fundamentals
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- Repositório
- ffsshhttiikk/opencode-agents-skills
- Última atividade na origem
- 28 de fevereiro de 2026 às 22:54
- Idioma detectado do SKILL.md
- inglês
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SKILL.md
Instruções da origem · Visualização somente leitura- name
- reinforcement
- description
- Reinforcement learning fundamentals
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"machine-learning-engineers","category":"artificial-intelligence"}
## 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
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