cd abp
   pip3 install -r requirement.txt
   python setup.py install

• Start Visdom: visdom

• 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

• 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