mods added

ad_hoc.py

@@ -0,0 +1,97 @@
+"""
+An example using the rover domain gym-style interface and the standard, included CCEA learning algorithms.
+This is a minimal example, showing the minimal Gym interface.
+"""
+import numpy as np
+import sys
+import multiprocessing as mp
+
+
+
+from rover_domain_core_gym import RoverDomainGym
+import code.ccea_2 as ccea
+import code.agent_domain_2 as domain
+import mods
+from teaming.learner import learner
+from sys import argv
+import pickle
+import tensorflow as tf
+
+
+def make_env(progress):
+
+    nagents=1
+
+    sim = RoverDomainGym(nagents,100)
+    #mods.recipePoi(sim)
+    obs=sim.reset()
+    #print(len(obs[0]))
+    #for i in range(sim.data["Recipe Size" ]):
+    #    sim.data["Item Held"][0][i]=progress[i]
+
+    #obs=reduce_state(obs)
+
+    sim.data["Coupling"]=1
+    sim.data['Number of Agents']=nagents
+    return sim
+
+def test1(trial):
+    env=make_env([1,1,1,1])
+    team=[0,0,1,1,2,2,3,3]
+    team=[0]
+    with tf.Session() as sess:
+
+        controller = learner(team,sess)
+        init=tf.global_variables_initializer()
+        sess.run(init)
+
+
+        for i in range(10001):
+            r=controller.run(env,i,0)# i%100 == -10)
+            print(i,max(r))
+            if i%1000==0 and 0:
+                controller.test(env)
+            if i%1000==0:
+                controller.save("logs/"+str(trial)+"t.pkl")
+            #print(r)
+'''
+test1(0)
+for i in range(0):
+    p=mp.Process(target=test1,args=(i,))
+    p.start()
+    #p.join()
+'''
+env=make_env(None)
+
+from time import sleep
+
+s=env.reset()
+s=s[:,4:][0]
+for i in range(100):
+
+    idx=1
+    loc=env.data["Poi Positions"][idx]
+    ang=env.data["Agent Orientations"][0]
+    pos=env.data["Agent Positions"][0]
+
+    heading=[loc[0]-pos[0],loc[1]-pos[1]]
+
+    trn=np.arccos( (heading[0]*ang[0]+heading[1]*ang[1])/( np.sqrt(heading[0]**2+heading[1]**2))* np.sqrt(ang[0]**2+ang[1]**2)  )    
+    trn/=4
+    spd=1.0
+
+    a=[spd,trn]
+
+    s,r,_,_=env.step([a])
+    s=s[:,4:][0]
+    print(i,r,trn,spd)
+    env.render()
+    sleep(0.033)
+
+
+
+
+
+
+
+

ad_hoc2.py

@@ -0,0 +1,234 @@
+"""
+An example using the rover domain gym-style interface and the standard, included CCEA learning algorithms.
+This is a minimal example, showing the minimal Gym interface.
+"""
+import numpy as np
+import sys
+import multiprocessing as mp
+
+
+
+from rover_domain_core_gym import RoverDomainGym
+import code.ccea_2 as ccea
+import code.agent_domain_2 as domain
+import mods
+from teaming.learner3 import learner
+from sys import argv
+import pickle
+import tensorflow as tf
+
+def rand_loc(n):
+    x,y=np.random.random(2)
+    pos=[[x,y]]
+    while len(pos)<6:
+        X,Y=np.random.random(2)
+        for x,y in pos:
+            dist=((X-x)**2.0+(Y-y)**2.0 )**0.5
+            if dist<0.2:
+                X=None 
+                break
+        if not X is None: 
+            pos.append([X,Y])
+
+    return np.array(pos)
+
+
+#print(vals)
+def make_env(team):
+    vals =np.array([0.1, 0.1, 0.5,0.3, 0.0, 0.0])
+
+    pos=np.array([
+            [0.0, 0.0],
+            [1.0, 1.0],
+            [0.0, 1.0],
+            [1.0, 0.5],
+            [0.0, 0.5],
+            [1.0, 0.0]
+        ])
+
+    #pos=rand_loc(6)#np.random.random((6,2))
+    #vals=np.random.random(6)/2.0
+    print(vals)
+    nagents=len(team)
+
+    sim = RoverDomainGym(nagents,100,pos,vals)
+    #mods.recipePoi(sim)
+    obs=sim.reset()
+    #print(len(obs[0]))
+    #for i in range(sim.data["Recipe Size" ]):
+    #    sim.data["Item Held"][0][i]=progress[i]
+
+    #obs=reduce_state(obs)
+
+    sim.data["Coupling"]=2
+    sim.data['Number of Agents']=nagents
+    return sim
+
+
+import time
+
+def test1(trial,frq):
+    frq=1
+    #print(np.random.get_state())[1]    
+    np.random.seed(int(time.time()*100000)%100000)
+    team=[0,0,1,1,2,2,3,3]
+    team=[0,1,2,2,1]
+
+    #team=[0,1,2,2,1,2,0,0]
+    #team=team+team
+    env=make_env(team)
+    with tf.compat.v1.Session() as sess:
+
+        controller = learner(team,sess,env)
+        init=tf.compat.v1.global_variables_initializer()
+        sess.run(init)
+
+
+        for i in range(10001):
+
+            if i%int(frq)==0:
+                controller.randomize()
+
+            r=controller.run(env,i,0)# i%100 == -10)
+            if i%10==0:
+                print(i,r[-1],controller.team)
+            if i%50==0 and 1:
+                controller.test(env)
+
+            if i%50==0:
+                #controller.save("tests/q"+str(frq)+"-"+str(trial)+".pkl")
+                #controller.save("logs/"+str(trial)+"r"+str(16)+".pkl")
+                #controller.save("tests/jj"+str(121)+"-"+str(trial)+".pkl")
+                controller.save("tests/evo"+str(121)+"-"+str(trial)+".pkl")
+            #print(r)
+            #print(r)
+
+
+def test2(trial,f):
+    #print(np.random.get_state())[1]    
+    np.random.seed(int(time.time()*100000)%100000)
+    team=[i for i in range(16)]
+    #team=[0,1,2,3,4]
+    env=make_env(team)
+    with tf.compat.v1Session() as sess:
+
+        controller = learner(team,sess)
+        init=tf.compat.v1.global_variables_initializer()
+        sess.run(init)
+
+
+        for i in range(10001):
+            r=controller.run(env,i,0)# i%100 == -10)
+            if i%10==0:
+                print(i,r[-1],controller.team)
+            if i%100==0 and 1:
+                controller.test(env)
+            if i%1000==0:
+                controller.save("logs/"+str(trial)+"v16.pkl")
+            #print(r)
+
+
+def test3(trial,f):
+    #print(np.random.get_state())[1]    
+    np.random.seed(int(time.time()*100000)%100000)
+    team=[0,1,2,0,0,0,0,0]
+    team=team+team
+    #team=[0,1,2,0,0]
+    env=make_env(team)
+    with tf.compat.v1.Session() as sess:
+
+        controller = learner(team,sess)
+        init=tf.compat.v1.global_variables_initializer()
+        sess.run(init)
+
+        controller.randomize()
+        for i in range(10001):
+            r=controller.run(env,i,0)# i%100 == -10)
+            if i%10==0:
+                print(i,r[-1],controller.team)
+            if i%100==0 and 1:
+                controller.test(env)
+            if i%1000==0:
+                controller.save("logs/"+str(trial)+"r8.pkl")
+            #print(r)
+
+def test4(trial,frq):
+    #print(np.random.get_state())[1]    
+    np.random.seed(int(time.time()*100000)%100000)
+    team=[0,0,1,1,2,2,3,3]
+    team=[0,1,2,2,1,1,0,0]
+    team=team+team
+    team=np.array([i%int(frq) for i in range(8)])
+    env=make_env(team)
+    with tf.compat.v1.Session() as sess:
+
+        controller = learner(team,sess)
+        init=tf.compat.v1.global_variables_initializer()
+        sess.run(init)
+
+
+        for i in range(10001):
+
+            if i%1==0:
+                controller.randomize()
+
+            r=controller.run(env,i,0)# i%100 == -10)
+            if i%10==0:
+                print(i,r[-1],controller.team)
+            if i%100==0 and 1:
+                controller.test(env)
+            if i%1000==0:
+                controller.save("tests/qq"+str(frq)+"-"+str(trial)+".pkl")
+
+            #print(r)
+def test5(trial,frq):
+    #print(np.random.get_state())[1]    
+    np.random.seed(int(time.time()*100000)%100000)
+    team=[0,0,1,1,2,2,3,3]
+    team=[0,1,2,2,1,1,0,0]
+    #team=team+team
+    #team=np.array([i%int(frq) for i in range(16)])
+    env=make_env(team)
+    with tf.compat.v1.Session() as sess:
+
+        controller = learner(team,sess)
+        init=tf.compat.v1.global_variables_initializer()
+        sess.run(init)
+        controller.put("poi vals",vals)
+
+        for i in range(10001):
+
+            if i%1==0:
+                controller.randomize()
+
+            r=controller.run(env,i,0)# i%100 == -10)
+            if i%10==0:
+                print(i,r[-1],controller.team)
+            if i%100==0 and 1:
+                controller.test(env)
+            if i%1000==0:
+                controller.save("tests/c"+str(frq)+"-"+str(trial)+".pkl")
+
+if 0:
+
+    test1(20)
+else:
+    f=sys.argv[1]
+    print(f)
+    f=int(f)
+    for i in range(4):
+        p=mp.Process(target=test1,args=(i+(8*f),f))
+        p.start()
+        time.sleep(0.01)
+        #p.join()
+
+#env=make_env(None)
+
+
+
+
+
+
+
+
+