sonny-dev / dqn-atari

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 0
30-day runs: -21
Model's Last Updated: April 09 2023
reinforcement-learning

Introduction of dqn-atari

Model Details of dqn-atari

DQN Agent playing SpaceInvadersNoFrameskip-v4

This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 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

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 dqn --env SpaceInvadersNoFrameskip-v4 -orga sonny-dev -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4  -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga sonny-dev -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4  -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sonny-dev
Hyperparameters
OrderedDict([('batch_size', 32),
             ('buffer_size', 100000),
             ('env_wrapper',
              ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
             ('exploration_final_eps', 0.01),
             ('exploration_fraction', 0.1),
             ('frame_stack', 4),
             ('gradient_steps', 1),
             ('learning_rate', 0.0001),
             ('learning_starts', 100000),
             ('n_timesteps', 1000000.0),
             ('optimize_memory_usage', False),
             ('policy', 'CnnPolicy'),
             ('target_update_interval', 1000),
             ('train_freq', 4),
             ('normalize', False)])

Runs of sonny-dev dqn-atari on huggingface.co

4
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
-21
30-day runs

More Information About dqn-atari huggingface.co Model

dqn-atari huggingface.co

dqn-atari huggingface.co is an AI model on huggingface.co that provides dqn-atari's model effect (), which can be used instantly with this sonny-dev dqn-atari model. huggingface.co supports a free trial of the dqn-atari model, and also provides paid use of the dqn-atari. Support call dqn-atari model through api, including Node.js, Python, http.

sonny-dev dqn-atari online free

dqn-atari huggingface.co is an online trial and call api platform, which integrates dqn-atari's modeling effects, including api services, and provides a free online trial of dqn-atari, you can try dqn-atari online for free by clicking the link below.

sonny-dev dqn-atari online free url in huggingface.co:

https://huggingface.co/sonny-dev/dqn-atari

dqn-atari install

dqn-atari is an open source model from GitHub that offers a free installation service, and any user can find dqn-atari on GitHub to install. At the same time, huggingface.co provides the effect of dqn-atari install, users can directly use dqn-atari installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

dqn-atari install url in huggingface.co:

https://huggingface.co/sonny-dev/dqn-atari

Url of dqn-atari

Provider of dqn-atari huggingface.co

sonny-dev
ORGANIZATIONS

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