parameter retuning

experiments_vrep.py

@@ -45,28 +45,28 @@ def log_callback(state):
 def experiment_filename(_source, _setup, _tag):
     return "{}_{}_{}".format(_source, _setup, _tag)
 
-
+# TODO folder creation
 if __name__ == '__main__':
     extra_tag = "_bd10"
     mode = [EXP_HEXAPOD_GOAL_REACHING]
 
     if EXP_HEXAPOD_GOAL_REACHING in mode:
-        extra_tag = "_300522_c"
+        extra_tag = "_030722_d"
 
         target_experiment_tag = EXP_HEXAPOD_GOAL_REACHING + extra_tag
         num_state_centers = 8
         sensor_dim = 4
 
         ##
-        total_iteration_n = 300000
-        continue_to =0
+        total_iteration_n = 200000
+        continue_to = 1
         # start_at = 1440
         start_at = -1
         record_jump = 100
         record_capacity = 40
         sens_window = 40
 
-        # command_override_start = 1000
+        # command_override_start = 1500
         command_override_start = 0
 
 

experiments_vrep_vis.py

@@ -31,11 +31,17 @@ def __call__(self, *args, **kwargs):
 # file_name = "hexapod_goal_reaching_ab_cpg_sync_frq400_str10.hdf5"
 # file_name = "hexapod_goal_reaching_ab_cpg_sync_frq420_str10.hdf5"
 # file_name = "hexapod_goal_reaching_ab_cpg_sync_frq533_str10.hdf5"
-file_name = "hexapod_goal_reaching_ab_cpg_sync_frq666_str10.hdf5"
-# file_name = "learning_hexapod_a_0.hdf5"
-
-# mode = [1]
-mode = [2]
+# file_name = "hexapod_goal_reaching_ab_cpg_sync_frq666_str10.hdf5"
+# file_name = "hexapod_goal_reaching_300522_c_1.hdf5"
+# file_name = "hexapod_goal_reaching_010722_c_1.hdf5"
+# file_name = "hexapod_goal_reaching_020722_a_0.hdf5"
+# file_name = "hexapod_goal_reaching_020722_b_0.hdf5"
+# file_name = "hexapod_goal_reaching_020722_c_0.hdf5"
+# file_name = "hexapod_goal_reaching_020722_d_0.hdf5"
+file_name = "hexapod_goal_reaching_030722_d_0.hdf5"
+
+mode = [1]
+# mode = [2]
 # mode = [3]
 # mode += [11]
 
@@ -65,22 +71,22 @@ def __call__(self, *args, **kwargs):
     ctr = Counter()
 
     # limit_cycle_controller.state_evolution(record, plt.figure(ctr()))
-    limit_cycle_controller.epicycle_evolution(record,  plt.figure(ctr()))
+    # limit_cycle_controller.epicycle_evolution(record,  plt.figure(ctr()))
 
     limit_cycle_controller_contextual.context_control_evol(record, plt.figure(ctr()))
-    limit_cycle_controller_contextual.context_quality_evol(record, plt.figure(ctr()), log=False)
-    limit_cycle_controller_contextual.motor_context_convergence(record, plt.figure(ctr()), squared_norm=True)
-    limit_cycle_controller_contextual.multi_control_outputs(record, plt.figure(ctr()))
+    # limit_cycle_controller_contextual.context_quality_evol(record, plt.figure(ctr()), log=False)
+    # limit_cycle_controller_contextual.motor_context_convergence(record, plt.figure(ctr()), squared_norm=True)
+    # limit_cycle_controller_contextual.multi_control_outputs(record, plt.figure(ctr()))
     #
-    limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=0, size=2)
-    limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=1, size=2)
+    # limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=0, size=2)
+    # limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=1, size=2)
     # limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=2, size=2)
     # limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=3, size=2)
     # limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=1, size=4)
     # limit_cycle_controller.y_mem_evolution(record, plt.figure(ctr()), sensor_m=2, size=4)
     #
     #
-    limit_cycle_controller.u_mem_evolution(record, plt.figure(ctr()), motor_n=0, size=2)
+    # limit_cycle_controller.u_mem_evolution(record, plt.figure(ctr()), motor_n=0, size=2)
     # limit_cycle_controller.u_mem_evolution(record, plt.figure(ctr()), motor_n=1, size=4)
     # limit_cycle_controller.u_mem_evolution(record, plt.figure(ctr()), motor_n=2, size=4)
     # robot_position_control.position_command_evolution(record, plt.figure(ctr()))
@@ -107,7 +113,7 @@ def __call__(self, *args, **kwargs):
     #
     # robot_goal_differential_control.diff_control_evol(record, plt.figure(ctr()))
     # robot_goal_differential_control.pid_evol(record, plt.figure(ctr()))
-    for i in [1, 2]:
+    for i in [0, 1]:
         limit_cycle_controller_contextual.model_context_parameter_convergence(record, plt.figure(ctr()), context=i)
 
         limit_cycle_controller_contextual.multi_io_matricies(record, plt.figure(ctr()), ctx=i, sensor_modewise_norming=True)
@@ -125,14 +131,14 @@ def __call__(self, *args, **kwargs):
         # limit_cycle_controller_contextual.bias_evolution(record, plt.figure(ctr()), ctx=i)
         # limit_cycle_controller_contextual.weight_matrix_evolution(record, plt.figure(ctr()), size=10,ctx=i)
 
-        limit_cycle_controller_contextual.phase_combined_sensory_evolution(record, plt.figure(ctr()), ctx=i)
+        # limit_cycle_controller_contextual.phase_combined_sensory_evolution(record, plt.figure(ctr()), ctx=i)
         # robot_position_control.u_leg_position(record, plt.figure(ctr()), ctx=i)
 
         # limit_cycle_controller_contextual.sensory_posterior_variance(record, plt.figure(ctr()), ctx=i)
         # robot_position_control.regularization_leg_budget(record, plt.figure(ctr()), ctx=i)
         # limit_cycle_controller_contextual.u_dif_evolution(record, plt.figure(ctr()), ctx=i)
         #
-        lrn_durs, ctr_durs, _ = limit_cycle_controller_contextual.get_context_management_durations(record, ctx=i)
+        # lrn_durs, ctr_durs, _ = limit_cycle_controller_contextual.get_context_management_durations(record, ctx=i)
         # for m in [0, 1, 3]:
         #     for lrn_dur in lrn_durs:
         #         limit_cycle_controller_contextual.posterior_sensory_variance_detail(record, plt.figure(ctr()), t=lrn_dur[1][0],

models/limit_cycle_controller_contextual.py

@@ -1,5 +1,6 @@
 from dynsys_framework.execution_helpers.model_executor_parameters import ModelExecutorParameters
 import numpy as np
+import models.temporary_constants as TS
 xp = np
 import time
 
@@ -507,8 +508,8 @@ def _weights_dyn(w, model_err, motor_diff, state_phase_activator, motor_phase_ac
 def d_motor_expected(motor_err, weights, motor_lr, ctx_ctr_sel, regularization, decay=0.):
     active = xp.einsum("imjd,nmcdx,ix->injcx", motor_err, weights, ctx_ctr_sel, optimize=True) * motor_lr
     if np.sum(ctx_ctr_sel) > 0:
-        return active * 0.0000001 - regularization * 0.01 #- decay * 0.00001
-        # return active * 0.000001 - regularization * 0.01 - decay * 0.00001 #FIXME
+        # return active * 0.0000001 - regularization * 0.01 #- decay * 0.00001
+        return active * TS.U_EXP_ACT_LR - regularization * 0.01 - decay * 0.00001 #FIXME
     else:
         return np.zeros(active.shape)
 
@@ -635,7 +636,7 @@ def model_quality_mean(quality_mean, quality):
     goes_down = quality < quality_mean
     goes_up = quality >= quality_mean
     # return (quality - quality_mean) * (goes_down * 0.02 + goes_up * 0.0005) #FIXME
-    return (quality - quality_mean) * (goes_down * 0.08 + goes_up * 0.001)
+    return (quality - quality_mean) * (goes_down * TS.QUALITY_MEAN_DOWN_LR + goes_up * TS.QUALITY_MEAN_UP_LR)
 
 
 def model_qiality_variance(quality_variance, quality_mean, quality):

models/temporary_constants.py

@@ -0,0 +1,12 @@
+
+# U_EXP_ACT_LR = 0.0001
+U_EXP_ACT_LR = 1e-6
+
+# QUALITY_MEAN_UP_LR = 0.001
+QUALITY_MEAN_UP_LR = 0.0005
+# QUALITY_MEAN_UP_LR = 0.005
+# QUALITY_MEAN_UP_LR = 0.0001
+
+
+# QUALITY_MEAN_DOWN_LR = 0.08
+QUALITY_MEAN_DOWN_LR = 0.02