# get size of state and action from environment
openerAgent = DQNAgent(testEnv.observation_space["opener"],
testEnv.action_space["opener"].n,
fillMemoryByPretrainedModel=True)
buyerAgent = DQNAgent(testEnv.observation_space["buyer"],
testEnv.action_space["buyer"].n,
fillMemoryByPretrainedModel=True)
sellerAgent = DQNAgent(testEnv.observation_space["seller"],
testEnv.action_space["seller"].n,
fillMemoryByPretrainedModel=True)
#buyerAgent = AlwaysHoldAgent(testEnv.observation_space["opener"], testEnv.action_space["opener"].n, agentName="buyer")
#sellerAgent = AlwaysHoldAgent(testEnv.observation_space["seller"], testEnv.action_space["seller"].n, agentName="seller")
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
#agent = agent.load_agent("../models/", "best_composite", dropSupportModel=True)
#agent = agent.loadPretrainedAgents(dir="../models/", baseName="super_resnet_34_{}_{}".format(symbol, timeframe))
agent = agent.loadPretrainedAgents(dir="../models/", baseName="qrl_resnet_34")
#agent = agent.load_agent("../models/", "checkpoint_composite")
print("start using agent")
dealsStatistics = agent.use_agent(testEnv)
###########################
import numpy as np
dealAvg = np.sum(dealsStatistics) / len(dealsStatistics)
dealStd = np.std(dealsStatistics)
print("Avg deal profit: {}".format(dealAvg))
print("Deal's std: {}".format(dealStd))
###########################
sumRew = 0
cumulativeReward = []
# get size of state and action from environment
openerAgent = DQNAgent(testEnv.observation_space["opener"],
testEnv.action_space["opener"].n,
fillMemoryByPretrainedModel=True)
#buyerAgent = DQNAgent(testEnv.observation_space["buyer"], testEnv.action_space["buyer"].n, fillMemoryByPretrainedModel=True)
#sellerAgent = DQNAgent(testEnv.observation_space["seller"], testEnv.action_space["seller"].n, fillMemoryByPretrainedModel=True)
buyerAgent = AlwaysHoldAgent(testEnv.observation_space["opener"],
testEnv.action_space["opener"].n,
agentName="buyer")
sellerAgent = AlwaysHoldAgent(testEnv.observation_space["seller"],
testEnv.action_space["seller"].n,
agentName="seller")
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
#agent = agent.load_agent("../models/", "best_composite", dropSupportModel=True)
agent = agent.loadPretrainedAgents(dir="../models/",
baseName="super_resnet_34_{}_{}".format(
symbol, timeframe))
#agent = agent.loadPretrainedAgents(dir="../models/", baseName="qrl_resnet_34")
#agent = agent.load_agent("../models/", "checkpoint_composite")
print("start using agent")
dealsStatistics = agent.use_agent(testEnv)
###########################
import numpy as np
dealAvg = np.sum(dealsStatistics) / len(dealsStatistics)
dealStd = np.std(dealsStatistics)
print("Avg deal profit: {}".format(dealAvg))
print("Deal's std: {}".format(dealStd))
###########################