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---
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
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