Parameters: PPO model, batch_size: 32, n_steps: 512, epochs: 10, gamma: 0.999, gae_lambda: 0.95, ent_coef: 0.01, total_timesteps=2000000

7125b9f about 1 year ago

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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: 289.88 +/- 15.48
	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)
	TODO: Add your code


	```python
	from stable_baselines3 import ...
	from huggingface_sb3 import load_from_hub

	...
	```