cd abp
pip3 install -r requirement.txt
python setup.py install
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Start Visdom:
visdom
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Train :
python -m abp.trainer.task_runner
It takes the following arguments:
Command Options Description --example EXAMPLE The example to run --adaptive ADAPTIVE The adaptive to use for the run --job-dir JOB_DIR The location to write tensorflow summaries --model-path MODEL_PATH The location to save the model --restore-model Restore the model instead of training a new model -r, --render Set if it should render the test episodes --training-episodes TRAINING_EPISODES Set the number of training episodes --test-episodes TEST_EPISODES Set the number of test episodes --decay-steps DECAY_STEPS Set the decay rate for exploration Minimal Example:
python -m abp.trainer.task_runner -f tasks/fruit_collection/hra/v1 -t abp.examples.open_ai.fruit_collection.hra -r
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Visualize Results:
tensorboard --logdir=tensorflow_summaries
To run the job using cloud ML use the following commands
now=$(date +"%Y%m%d_%H%M%S")
JOB_NAME="abp_tictactoe_$now"
TRAINER_PACKAGE_PATH="/path-to-abp/abp"
TRAINER_CONFIG_PATH="/path-to-abp/abp/trainer/cloudml-gpu.yml"
MAIN_TRAINER_MODULE="abp.trainer.task_runner"
JOB_DIR="gs://path-to-job-dir"
MODEL_PATH="gs://path-to-model"
EXAMPLE="tictactoe"
ADAPTIVE="hra"
TRAINING_EPISODES=1000
DECAY_STEPS=250
gcloud ml-engine jobs submit training $JOB_NAME \
--package-path $TRAINER_PACKAGE_PATH \
--module-name $MAIN_TRAINER_MODULE \
--job-dir $JOB_DIR \
--region us-east1 \
--stream-logs \
--config=$TRAINER_CONFIG_PATH \
-- \
--example $EXAMPLE \
--adaptive $ADAPTIVE \
--model-path $MODEL_PATH \
--training-episodes $TRAINING_EPISODES \
--decay-steps $DECAY_STEPS