---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: LunarLander-v2
      type: LunarLander-v2
    metrics:
    - type: mean_reward
      value: 266.17 +/- 19.58
      name: mean_reward
      verified: false
---

# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)



```python

# https://stackoverflow.com/questions/72483775/stable-baselines3-ppo-how-to-change-clip-range-parameter-during-training
def lrsched():
  def reallr(progress):
    lr = 0.004
    if progress < 0.85:
      lr = 0.0005
    if progress < 0.66:
      lr = 0.00025
    if progress < 0.33:
      lr = 0.0001
    return lr
  return reallr
model = PPO('MlpPolicy', env, n_steps = 1024, batch_size = 64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01, verbose=1, learning_rate=lrsched())

model.learn(total_timesteps=1000000)

model_name = "ppo-LunarLander-v2"
model.save(model_name)

eval_env = Monitor(gym.make("LunarLander-v2"))

# Evaluate the model with 10 evaluation episodes and deterministic=True
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)

# Print the results
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# mean_reward=245.30 +/- 50.161170246383584
...
```

## Diffs

* Added a variable learning rate