--- a
+++ b/cmaes/optim_L2M2019Ctrl_2D.py
@@ -0,0 +1,85 @@
+from osim.env import L2M2019CtrlEnv
+from osim.control.osim_loco_reflex_song2019 import OsimReflexCtrl
+
+import sys
+import numpy as np
+
+trial_name = 'trial_190501_L2M2019CtrlEnv_2D_'
+
+class CMATrain(object):
+    def __init__(self, ):
+        self.n_f_call = 0
+        self.best_total_reward = -np.inf
+
+        self.flag_model = '2D'
+        self.flag_ctrl_mode = '2D' # use 2D
+        self.seed = None
+        self.difficulty = 0
+        self.sim_dt = 0.01
+        self.sim_t = 20
+        self.timstep_limit = int(round(self.sim_t/self.sim_dt))
+
+        self.locoCtrl = OsimReflexCtrl(mode=self.flag_ctrl_mode, dt=self.sim_dt)
+        self.env = L2M2019CtrlEnv(locoCtrl=self.locoCtrl, seed=self.seed, difficulty=self.difficulty, visualize=False)
+        self.env.change_model(model=self.flag_model, difficulty=self.difficulty, seed=self.seed)
+        
+    def f(self, params):
+        self.n_f_call += 1
+
+        try:
+            observation = self.env.reset(project=True, seed=self.seed)
+        except Exception as e_msg:
+            print("simulation error!!!")
+            print(e_msg)
+            #import pdb; pdb.set_trace()
+            return 0
+        self.env.spec.timestep_limit = self.timstep_limit+100
+
+        total_reward = 0
+        error_sim = 0;
+        t = 0
+        for i in range(self.timstep_limit+100):
+            t += self.sim_dt
+
+            observation, reward, done, info = self.env.step(params, project=True)
+            total_reward += reward
+
+            if done:
+                break
+
+        print('    sim #{}: score={} time={}sec #step={}'.format(self.n_f_call, total_reward, t, self.env.footstep['n']))
+
+        if self.best_total_reward < total_reward:
+            filename = "./data/cma/" + trial_name + "best.txt"
+            print("")
+            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 -total_reward  # minimization
+
+params = np.ones(37)
+#params = np.loadtxt('./data/cma/trial_181029_walk_3D_noStand_8_best.txt')
+
+if __name__ == '__main__':
+    prob = CMATrain()
+
+    from cmaes.solver_cma import CMASolver
+    solver = CMASolver(prob)
+
+    solver.options.set("popsize", 16) # 8 = 4 + floor(3*log(37))
+    solver.options.set("maxiter", 400)
+    solver.options.set("verb_filenameprefix", 'data/cma/' + trial_name)
+    solver.set_verbose(True)
+
+    x0 = params
+    sigma = .01
+
+    res = solver.solve(x0, sigma)
+    print(res)