mutliple run script and auto experiment name

scripts/run_multiple.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+gpuList=(1 2 3 4 5 6 7)
+(
+for i in ${gpuList[@]}; do 
+   CUDA_VISIBLE_DEVICES=$i python train.py \
+    --domain_name reacher \
+    --task_name easy \
+    --encoder_type pixel \
+    --action_repeat 4 \
+    --save_tb --pre_transform_image_size 100 --image_size 84 \
+    --work_dir ./tmp/curl/reacher_easy \
+    --agent curl_sac --frame_stack 3 \
+    --seed -1 --critic_lr 1e-3 --actor_lr 1e-3 --eval_freq 20000 --batch_size 128 --num_train_steps 1000000 &
+done
+)
+echo "Launched CURL script"

train.py

@@ -151,7 +151,7 @@ def make_agent(obs_shape, action_shape, args, device):
 def main():
     args = parse_args()
     if args.seed == -1: 
-        args.__dict__["seed"] = np.random.randint(1,1000)
+        args.__dict__["seed"] = np.random.randint(1,1000000)
     utils.set_seed_everywhere(args.seed)
     env = dmc2gym.make(
         domain_name=args.domain_name,
@@ -169,6 +169,13 @@ def main():
     # stack several consecutive frames together
     if args.encoder_type == 'pixel':
         env = utils.FrameStack(env, k=args.frame_stack)
+    
+    # make directory
+    ts = time.gmtime() 
+    ts = time.strftime("%m-%d", ts)    
+    env_name = args.domain_name + '-' + args.task_name
+    exp_name = env_name + '-' + ts + '-im' + str(args.image_size) +'-b'  + str(args.batch_size) + '-s' + str(args.seed)  
+    args.work_dir = args.work_dir + '/'  + exp_name
 
     utils.make_dir(args.work_dir)
     video_dir = utils.make_dir(os.path.join(args.work_dir, 'video'))