--- a
+++ b/cmaes/optim_par_L2M2019Ctrl.py
@@ -0,0 +1,114 @@
+from osim.env import L2M2019Env
+from osim.control.osim_loco_reflex_song2019 import OsimReflexCtrl
+from joblib import Parallel, delayed
+
+import sys
+import numpy as np
+
+trial_name = 'trial_190505_L2M2019CtrlEnv_d0_'
+
+params = np.ones(45)
+#params = np.loadtxt('./optim_data/cma/trial_181029_walk_3D_noStand_8_best.txt')
+N_POP = 16 # 8 = 4 + floor(3*log(45))
+N_PROC = 2
+TIMEOUT = 10*60
+      
+init_pose = np.array([1.5, .9, 10*np.pi/180, # forward speed, pelvis height, trunk lean
+        -3*np.pi/180, -30*np.pi/180, -10*np.pi/180, 10*np.pi/180, # [right] hip abduct, hip extend, knee extend, ankle extend
+        -3*np.pi/180, 5*np.pi/180, -40*np.pi/180, -0*np.pi/180]) # [left] hip abduct, hip extend, knee extend, ankle extend
+  
+def f_ind(n_gen, i_worker, params):
+    flag_model = '3D'
+    flag_ctrl_mode = '3D' # use 2D
+    seed = None
+    difficulty = 0
+    sim_dt = 0.01
+    sim_t = 20
+    timstep_limit = int(round(sim_t/sim_dt))
+
+    init_error = True
+    error_count = 0
+    while init_error:
+        try:
+            locoCtrl = OsimReflexCtrl(mode=flag_ctrl_mode, dt=sim_dt)
+            env = L2M2019Env(seed=seed, difficulty=difficulty, visualize=False)
+            env.change_model(model=flag_model, difficulty=difficulty, seed=seed)
+            obs_dict = env.reset(project=True, seed=seed, init_pose=init_pose, obs_as_dict=True)
+            init_error = False
+        except Exception as e_msg:
+            error_count += 1
+            print('\ninitialization error (x{})!!!'.format(error_count))
+            #print(e_msg)
+            #import pdb; pdb.set_trace()
+    env.spec.timestep_limit = timstep_limit+100
+
+    total_reward = 0
+    error_sim = 0;
+    t = 0
+    for i in range(timstep_limit+100):
+        t += sim_dt
+
+        locoCtrl.set_control_params(params)
+        action = locoCtrl.update(obs_dict)
+        obs_dict, reward, done, info = env.step(params, project=True, obs_as_dict=True)
+        total_reward += reward
+
+        if done:
+            break
+
+    print('\n    gen#={} sim#={}: score={} time={}sec #step={}'.format(n_gen, i_worker, total_reward, t, env.footstep['n']))
+
+    return total_reward  # minimization
+
+
+class CMATrainPar(object):
+    def __init__(self, ):
+        self.n_gen = 0
+        self.best_total_reward = -np.inf
+
+    def f(self, v_params):
+        self.n_gen += 1
+        timeout_error = True
+        error_count = 0
+        while timeout_error:
+            try:
+                v_total_reward = Parallel(n_jobs=N_PROC, timeout=TIMEOUT)\
+                (delayed(f_ind)(self.n_gen, i, p) for i, p in enumerate(v_params))
+                timeout_error = False
+            except Exception as e_msg:
+                error_count += 1
+                print('\ntimeout error (x{})!!!'.format(error_count))
+                #print(e_msg)
+
+        for total_reward in v_total_reward:
+            if self.best_total_reward  < total_reward:
+                filename = "./optim_data/cma/" + trial_name + "best_w.txt"
+                print("\n")
+                print("----")
+                print("update the best score!!!!")
+                print("\tprev = %.8f" % self.best_total_reward )
+                print("\tcurr = %.8f" % total_reward)
+                print("\tsave to [%s]" % filename)
+                print("----")
+                print("")
+                self.best_total_reward  = total_reward
+                np.savetxt(filename, params)
+
+        return [-r for r in v_total_reward]
+
+if __name__ == '__main__':
+    prob = CMATrainPar()
+
+    from cmaes.solver_cma import CMASolverPar
+    solver = CMASolverPar(prob)
+
+    solver.options.set("popsize", N_POP)
+    solver.options.set("maxiter", 400)
+    solver.options.set("verb_filenameprefix", 'optim_data/cma/' + trial_name)
+    solver.set_verbose(True)
+
+    x0 = params
+    sigma = .01
+
+    res = solver.solve(x0, sigma)
+    print(res)