A High Level Python Deep Reinforcement Learning library. Great for beginners, prototyping and quickly comparing algorithms
UNDER CONSTRUCTION!
Do not use yet!
| System | 3.5 | 3.6 | 3.7 |
|---|---|---|---|
| Linux CPU |  |
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— |
| Linux GPU | |
|
— |
| Windows CPU / GPU | — |  |
— |
| Linux (ppc64le) CPU |  |
— |  |
| Linux (ppc64le) GPU |  |
— |  |
Installation
Run the following to install:
pip install drlkit
Usage
import numpy as np
from drlkit import TorchAgent, Plot, EnvironmentWrapper
ENV_NAME = "LunarLander-v2"
env = EnvironmentWrapper(ENV_NAME)
agent = TorchAgent(state_size=8, action_size=env.env.action_space.n, seed=0)
# Train the agent
env.fit(agent, n_episodes=1000)
# See the results
Plot.basic_plot(np.arange(len(env.scores)), env.scores, xlabel='Episode #', ylabel='Score')
# Play untrained agent
env.load_model(agent, env="LunarLander", elapsed_episodes=3000)
env.play(num_episodes=10, trained=False)
# Play trained agent
env.play(num_episodes=10, trained=True)
1 maintainer
Metadata
1 maintainer
Release files for drlkit 0.1.7
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| drlkit-0.1.7.tar.gz | 8.7 kB | Without provenance | Details for drlkit-0.1.7.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| drlkit-0.1.7-py3-none-any.whl | Python 3 | none | any | Details for drlkit-0.1.7-py3-none-any.whl |
Total release size: 20.4 kB
Release files / drlkit-0.1.7.tar.gz
| Download URL | drlkit-0.1.7.tar.gz |
|---|---|
| Size | 8.7 kB |
| Tags | Source |
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Release files / drlkit-0.1.7-py3-none-any.whl
| Download URL | drlkit-0.1.7-py3-none-any.whl |
|---|---|
| Size | 11.7 kB |
| Tags | Python 3 |
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Security for drlkit 0.1.7
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