gistfile1.txt

#### dev-horovod
python run_experiment_mm_raw.py \
  --workers_csv_path=sandbox/a_2.csv \
  --level_name=PongNoFrameskip-v4 \
  --batch_size=32 \
  --entropy_cost=0.0033391318945337044 \
  --learning_rate=0.00031 \
  --total_environment_frames=10000000000 \
  --reward_clipping=soft_asymmetric

## 
python run_experiment_mm_raw.py \
  --workers_csv_path=sandbox/aa.csv \
  --level_name=BreakoutNoFrameskip-v4 \
  --agent_name=SimpleConvNetAgent \
  --batch_size=32 \
  --unroll_length=20 \
  --entropy_cost=0.01 \
  --learning_rate=0.0006 \
  --total_environment_frames=200000000 \
  --reward_clipping=abs_one \
  --gradients_clipping=40.0 \
  --queue_capacity=250
  
python run_experiment_mm_raw.py \
  --workers_csv_path=sandbox/aa.csv \
  --level_name=BreakoutNoFrameskip-v4 \
  --agent_name=ResNetLSTMAgent \
  --batch_size=32 \
  --unroll_length=40 \
  --entropy_cost=0.01 \
  --learning_rate=0.0003 \
  --total_environment_frames=400000000 \
  --reward_clipping=abs_one \
  --gradients_clipping=-1 \
  --queue_capacity=1

####
mpirun --allow-run-as-root -np 2 \
    -H localhost:2 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
    -mca pml ob1 -mca btl ^openib \
    python tensorflow_mnist.py

####
python run_experiment_mm_raw.py \
  --cluster_csv_path=sandbox/tmp2.csv \
  --level_name=PongNoFrameskip-v4 \
  --num_action_repeats=1 \
  --batch_size=32 \
  --entropy_cost=0.0033391318945337044 \
  --learning_rate=0.00031 \
  --total_environment_frames=10000000000 \
  --reward_clipping=soft_asymmetric


python run_experiment_mm_raw.py \
  --cluster_csv_path=sandbox/local_cluster_example.csv \
  --level_name=PongNoFrameskip-v4 \
  --num_action_repeats=1 \
  --batch_size=1 \
  --entropy_cost=0.0033391318945337044 \
  --learning_rate=0.00031 \
  --total_environment_frames=10000000000 \
  --reward_clipping=soft_asymmetric

python run_experiment_mm_raw.py \
  --cluster_csv_path=sandbox/c1.csv \
  --level_name=PongNoFrameskip-v4 \
  --num_action_repeats=1 \
  --batch_size=32 \
  --entropy_cost=0.0033391318945337044 \
  --learning_rate=0.00031 \
  --total_environment_frames=10000000000 \
  --reward_clipping=soft_asymmetric
  

python run_experiment_mm_raw.py \
  --cluster_csv_path=sandbox/c1.csv \
  --num_action_repeats=1 \
  --batch_size=32 \
  --entropy_cost=0.0033391318945337044 \
  --learning_rate=0.00031 \
  --total_environment_frames=10000000000 \
  --reward_clipping=soft_asymmetric