Reinforcement Learning Codebase
Modular codebase for reinforcement learning models training, testing and visualization.
Example for recorded envrionment on various RL agents.
MountainCar-v0 | Pendulum-v0 | VideoPinball-v0 | Tennis-v0 |
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Requirements
- Run setup scripts to install all dependencies and environments
./setup.sh
Quick Start
# start training
python train.py --sys ... --hparams ... --output_dir ...
# run tensorboard
tensorboard --logdir ...
# test agnet
python train.py --sys ... --hparams ... --output_dir ... --training False --render True
Hyper-parameters
Check init_flags(), defaults.py for default hyper-parameters, and check https://github.com/for-ai/rl/blob/master/rl/hparams/dqn.py agent specific hyper-parameters examples.
hparams
: Which hparams to use, defined under rl/hparamssys
: Which system environment to use.env
: Which RL environment to use.output_dir
: The directory for model checkpoints and TensorBoard summary.train_steps
:, Number of steps to train the agent.test_episodes
: Number of episodes to test the agent.eval_episodes
: Number of episodes to evaluate the agent.training
: train or test agent.copies
: Number of independent training/testing runs to do.render
: Render game play.record_video
: Record game play.num_workers
, number of workers.