optimizer = optim.Adam(policyNet.parameters(), lr=config['learningRate'])
agent = DQNAgent(config, policyNet, targetNet, env, optimizer,
torch.nn.MSELoss(reduction='none'), N_A)
xSet = np.linspace(-1, 1, 100)
policy = np.zeros_like(xSet)
for i, x in enumerate(xSet):
policy[i] = agent.getPolicy(np.array([x]))
np.savetxt('StabilizerPolicyBeforeTrain.txt', policy, fmt='%d')
#agent.perform_random_exploration(10)
agent.train()
#storeMemory = ReplayMemory(100000)
agent.testPolicyNet(100)
#storeMemory.write_to_text('testPolicyMemory.txt')
def customPolicy(state):
x = state[0]
# move towards negative
if x > 0.1:
action = 2
# move towards positive
elif x < -0.1:
action = 1
# do not move
else:
action = 0
return action
'phiIdx' + str(phiIdx) + '.txt',
policy,
fmt='%d',
delimiter='\t')
np.savetxt('DynamicMazeValueAfterTrain' + config['mapName'] +
'phiIdx' + str(phiIdx) + '.txt',
value,
fmt='%.3f',
delimiter='\t')
torch.save(
{
'model_state_dict': agent.policyNet.state_dict(),
'optimizer_state_dict': agent.optimizer.state_dict(),
}, config['saveModelFile'])
if testFlag:
config['loadExistingModel'] = True
if config['loadExistingModel']:
checkpoint = torch.load(config['saveModelFile'])
agent.policyNet.load_state_dict(checkpoint['model_state_dict'])
agent.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
recorder = TrajRecorder()
agent.env.agent.config['stochMoveFlag'] = True
agent.testPolicyNet(100, recorder)
recorder.write_to_file(config['mapName'] + 'TestTraj.txt')
#plotPolicy(policy, N_A)
optimizer,
torch.nn.MSELoss(reduction='none'),
N_A,
config=config)
xSet = np.linspace(-1, 1, 100)
policy = np.zeros_like(xSet)
for i, x in enumerate(xSet):
policy[i] = agent.getPolicy(np.array([x]))
np.savetxt('StabilizerPolicyBeforeTrain.txt', policy, fmt='%d')
#agent.perform_random_exploration(10)
agent.train()
storeMemory = ReplayMemory(100000)
agent.testPolicyNet(100, storeMemory)
storeMemory.write_to_text('testPolicyMemory.txt')
def customPolicy(state):
x = state[0]
# move towards negative
if x > 0.1:
action = 2
# move towards positive
elif x < -0.1:
action = 1
# do not move
else:
action = 0
return action
