test_gpu2

#!/bin/bash

if [ -z "$1" ]
  then echo "Please provide the name of the game, e.g.  ./run_gpu breakout [<version>] [<seed>]"; exit 0
fi
ENV=$1
FRAMEWORK="alewrap"

game_path=$PWD"/roms/"
env_params="useRGB=true"
agent="NeuralQLearner"
n_replay=1
netfile="\"convnet_atari3\""
update_freq=4
actrep=4
discount=0.99
seed=3
if [ -z "$3" ]; then
  seed=1
else
  seed=$3
  
fi
learn_start=0
pool_frms_type="\"max\""
pool_frms_size=2
initial_priority="false"
replay_memory=1000000
eps_end=0.1
eps_endt=replay_memory
lr=0.00025
agent_type="DQN3_0_1"
preproc_net="\"net_downsample_2x_full_y\""
agent_name=$agent_type"_"$1"_FULL_Y_A"
agent_nameB=$agent_type"_"$1"_FULL_Y_B"
datas_file="$ENV.csv"
if [ -z "$2" ]; then
  network_file="$agent_name.t7"
  network_fileB="$agent_nameB.t7"
  gif_file="../sessions/$ENV.gif"
  csv_file="../sessions/$ENV.csv"
  version=0
else
  network_file="${agent_name}_$2.t7"
  network_fileB="${agent_nameB}_$2.t7"
  gif_file="../sessions/${ENV}_$2.gif"
  csv_file="../sessions/${ENV}_$2.csv"
  version=$2
fi
state_dim=7056
ncols=1
agent_params="lr="$lr",ep=1,ep_end="$eps_end",ep_endt="$eps_endt",discount="$discount",hist_len=4,learn_start="$learn_start",replay_memory="$replay_memory",update_freq="$update_freq",n_replay="$n_replay",network="$netfile",preproc="$preproc_net",state_dim="$state_dim",minibatch_size=32,rescale_r=1,ncols="$ncols",bufferSize=512,valid_size=500,target_q=10000,clip_delta=1,min_reward=-1,max_reward=1"
gpu=0
random_starts=30
pool_frms="type="$pool_frms_type",size="$pool_frms_size
num_threads=4
cd dqn
args="-framework $FRAMEWORK -game_path $game_path -name $agent_name -nameB $agent_nameB -env $ENV -env_params $env_params -agent $agent -agent_params $agent_params -actrep $actrep -gpu $gpu -random_starts $random_starts -pool_frms $pool_frms -seed $seed -threads $num_threads -verbose 1 -network $network_file -networkB $network_fileB -gif_file $gif_file -csv_file $csv_file -version $version -datas_file $datas_file"
  ../torch/bin/qlua test_2agent.lua $args