Model: MariusBerg/DeepRL-LunarLander
Author: MariusBerg
Pipeline: reinforcement-learning
Library: stable-baselines3
Tags: stable-baselines3, LunarLander-v2, deep-reinforcement-learning, reinforcement-learning, model-index, region:us
Card metadata: library_name: stable-baselines3
tags: LunarLander-v2, deep-reinforcement-learning, reinforcement-learning, stable-baselines3
model-index: name: The PPO agent uses an MLP policy: input layer (8 obs values) → hidden layers (2 fully connected layers of 64 units with nonlinearities) → output layer split into a policy head (action probabilities over 4 actions) and a value head (state value estimate); this setup allows the agent to learn both which action to take and how good the current state is.
results: task: type: reinforcement-learning
name: reinforcement-learning
dataset: name: LunarLander-v2
type: LunarLander-v2
metrics: type: mean_reward
value: 279.85 +/- 17.58
name: mean_reward
verified: no
Likes: 0 | All-time downloads: 51
Created: 2025-09-11 10:43:23