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Advanced Training Environments for Interactive AI Agents

Agents need interactive worlds for reinforcement learning; high-quality simulations replace costly real-world data collection.

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Implementation Steps

  1. Basic environment setup

    import gymnasium as gym

env = gym.make("WebEnv-v0")

Custom web simulation


2. **Agent interaction loop**
```python
obs, _ = env.reset()
action = agent.act(obs)
next_obs, reward, terminated, truncated, info = env.step(action)
  1. Data augmentation: Mix simulated rollouts with video demonstrations for better transfer.
  2. Evaluation: Run repeated episodes and measure success rate, latency, and reward stability.
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