Editing Reinforcement Learning (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Setting up a basic RL training loop with Gymnasium and Stable-Baselines3:'''

<syntaxhighlight lang="python">
import gymnasium as gym
from stable_baselines3 import PPO

# Create environment
env = gym.make("CartPole-v1")

# Instantiate agent with PPO algorithm
model = PPO(
    "MlpPolicy",
    env,
    learning_rate=3e-4,
    n_steps=2048,
    batch_size=64,
    n_epochs=10,
    gamma=0.99,
    verbose=1
)

# Train for 100,000 steps
model.learn(total_timesteps=100_000)

# Evaluate
obs, _ = env.reset()
for _ in range(1000):
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, done, truncated, _ = env.step(action)
    if done or truncated:
        obs, _ = env.reset()
</syntaxhighlight>

; Common RL algorithm selection guide
: '''Discrete actions + simple environments''' → DQN, Double DQN
: '''Continuous control (robotics, locomotion)''' → SAC (Soft Actor-Critic), TD3
: '''General purpose, on-policy''' → PPO, A3C
: '''RLHF for LLMs''' → PPO with KL divergence penalty from reference model
: '''Game environments''' → AlphaZero-style MCTS + RL for perfect-information games
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