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README.md | 3 years ago | |
cartpole_agent.py | 4 years ago | |
cartpole_model.py | 4 years ago | |
policy_gradient.py | 4 years ago | |
train.py | 3 years ago |
Train an agent with PARL to solve the CartPole problem, a classical benchmark in RL. Dygraph version of QuickStart
# Install dependencies
pip install paddlepaddle
# Or use Cuda: pip install paddlepaddle-gpu
pip install gym
git clone https://github.com/PaddlePaddle/PARL.git
cd PARL
pip install .
# Train model
cd examples/EagerMode/QuickStart/
python train.py
The agent can get around 200 points in a few minutes.
PARL 是一个高性能、灵活的强化学习框架
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