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sb3/
a2c-LunarLander-v3

Name: a2c-LunarLander-v3
Availability: InStock

stable-baselines3

LunarLander-v3

deep-reinforcement-learning

reinforcement-learning

model-index

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A2C Agent playing LunarLander-v3

This is a trained model of a A2C agent playing LunarLander-v3 using the stable-baselines3 library and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx

Install the RL Zoo (with SB3 and SB3-Contrib):

pip install rl_zoo3

# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo a2c --env LunarLander-v3 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLander-v3  -f logs/

If you installed the RL Zoo3 via pip (pip install rl_zoo3), from anywhere you can do:

python -m rl_zoo3.load_from_hub --algo a2c --env LunarLander-v3 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLander-v3  -f logs/

Training (with the RL Zoo)

python -m rl_zoo3.train --algo a2c --env LunarLander-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env LunarLander-v3 -f logs/ -orga sb3

Hyperparameters

python

OrderedDict([('ent_coef', 1e-05),
             ('gamma', 0.995),
             ('learning_rate', 'lin_0.00083'),
             ('n_envs', 8),
             ('n_steps', 5),
             ('n_timesteps', 200000.0),
             ('policy', 'MlpPolicy'),
             ('normalize', False)])

Environment Arguments

{'render_mode': 'rgb_array'}

sb3/a2c-LunarLander-v3