penalty_para = { #"weight_matrix": [0, 0.002], "weight_matrix": [0, 0.004], "S": [0.0001, 0.0008], #"S": [0.0003, 0.0024], #"S": [0.0000, 0.000], } thickner_para = { "dt": 1, "noise_in": False, "noise_p": 0.002, "noise_type": 3, 'time_length': 20, # 浓密机每次仿真20秒 } from Control_Exp1001.demo.thickener_chinese.common import exp_name exp_name.set_exp_name('HDP_Replay') EXP_NAME = exp_name.get_exp_name() img_path = os.path.join('../images', EXP_NAME) if not os.path.exists(img_path): os.mkdir(img_path) def new_hdp(capacity=2, batch_size=2): predict_round = 3000 gamma = 0.6 replay_hdp = ReplayBuffer(capacity=capacity) env_HDP = Thickener( noise_p=0.03, noise_in=True, ) exploration = No_Exploration()
#"weight_matrix": [0, 0.002], #"weight_matrix": [0, 0.010], "weight_matrix": [0, 0.0040], "S": [0.0001, 0.0008], #"S": [0.0003, 0.0024], #"S": [0.0000, 0.000], } thickner_para = { "dt": 1, "noise_in": False, "noise_p": 0.002, "noise_type": 3, 'time_length': 20, # 浓密机每次仿真20秒 } from Control_Exp1001.demo.thickener_chinese.common import exp_name exp_name.set_exp_name('VIandHDPandDHP') EXP_NAME = exp_name.get_exp_name() img_path = os.path.join('../images', EXP_NAME) if not os.path.exists(img_path): os.mkdir(img_path) def new_vi(): capacity = 2 predict_round = 3000 u_optim = 'sgd' gamma = 0.6 replay_vi = ReplayBuffer(capacity=capacity) env_VI = Thickener( noise_p=0.03, noise_in=True,
#"weight_matrix": [0, 0.002], #"weight_matrix": [0, 0.010], "weight_matrix": [0, 0.0040], "S": [0.0001, 0.0008], #"S": [0.0003, 0.0024], #"S": [0.0000, 0.000], } thickner_para = { "dt": 1, "noise_in": False, "noise_p": 0.002, "noise_type": 3, 'time_length': 20, # 浓密机每次仿真20秒 } from Control_Exp1001.demo.thickener_chinese.common import exp_name exp_name.set_exp_name('ILPL') EXP_NAME = exp_name.get_exp_name() img_path = os.path.join('../images', EXP_NAME) if not os.path.exists(img_path): os.mkdir(img_path) def new_ILPL(): predict_round = 3000 gamma = 0.6 replay_ILPL = ReplayBuffer(capacity=4) env_ILPL = Thickener( noise_p=0.03, noise_in=True, ) exploration = No_Exploration()
#"weight_matrix": [0, 0.002], #"weight_matrix": [0, 0.010], "weight_matrix": [0, 0.0040], "S": [0.0001, 0.0008], #"S": [0.0003, 0.0024], #"S": [0.0000, 0.000], } thickner_para = { "dt": 1, "noise_in": False, "noise_p": 0.002, "noise_type": 3, 'time_length': 20, # 浓密机每次仿真20秒 } from Control_Exp1001.demo.thickener_chinese.common import exp_name exp_name.set_exp_name('VIandHDPandDHPandILPL') EXP_NAME = exp_name.get_exp_name() img_path = os.path.join('../images', EXP_NAME) if not os.path.exists(img_path): os.mkdir(img_path) def new_vi(): capacity = 2 predict_round = 3000 u_optim = 'sgd' gamma = 0.6 replay_vi = ReplayBuffer(capacity=capacity) env_VI = Thickener( noise_p=0.03, noise_in=True,
penalty_para = { #"weight_matrix": [0, 0.002], "weight_matrix": [0, 0.004], "S": [0.0001, 0.0008], #"S": [0.0003, 0.0024], #"S": [0.0000, 0.000], } thickner_para = { "dt":1, "noise_in": False, "noise_p": 0.002, "noise_type": 3, 'time_length': 20,# 浓密机每次仿真20秒 } from Control_Exp1001.demo.thickener_chinese.common import exp_name exp_name.set_exp_name('VI_Replay') EXP_NAME = exp_name.get_exp_name() img_path = os.path.join('../images',EXP_NAME) if not os.path.exists(img_path): os.mkdir(img_path) def new_vi(capacity=2, batch_size=2): capacity = capacity predict_round=3000 u_optim='sgd' gamma=0.6 replay_vi = ReplayBuffer(capacity=capacity) env_VI = Thickener( noise_p=0.03, noise_in=True,
penalty_para = { #"weight_matrix": [0, 0.002], "weight_matrix": [0, 0.004], "S": [0.0001, 0.0008], #"S": [0.0003, 0.0024], #"S": [0.0000, 0.000], } thickner_para = { "dt": 1, "noise_in": False, "noise_p": 0.002, "noise_type": 3, 'time_length': 20, # 浓密机每次仿真20秒 } from Control_Exp1001.demo.thickener_chinese.common import exp_name exp_name.set_exp_name('VIandHDP') EXP_NAME = exp_name.get_exp_name() def new_vi(): capacity = 2 predict_round = 3000 u_optim = 'sgd' gamma = 0.6 replay_vi = ReplayBuffer(capacity=capacity) env_VI = Thickener( noise_p=0.03, noise_in=True, ) exploration = No_Exploration()