torch.nn.MSELoss(reduction='none'),
N_A,
stateProcessor=stateProcessor,
experienceProcessor=experienceProcessor)
trainFlag = True
testFlag = True
if trainFlag:
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'])
if config['loadCheckpointFlag']:
agent.load_checkpoint(config['loadCheckpointPrefix'])
plotPolicyFlag = True
if plotPolicyFlag:
for phiIdx in range(8):
phi = phiIdx * np.pi / 4.0
policy = deepcopy(env.mapMat).astype(np.long)
value = deepcopy(env.mapMat)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.mapMat[i, j] == 1:
policy[i, j] = -1
value[i, j] = -1
else:
sensorInfo = env.agent.getSensorInfoFromPos(
stateProcessor=stateProcessor, config=config)
trainFlag = True
testFlag = True
if trainFlag:
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'])
if config['loadCheckpoint']:
agent.load_checkpoint(config['checkpointTag'])
plotPolicyFlag = True
if plotPolicyFlag:
for phiIdx in range(8):
phi = phiIdx * np.pi/4.0
policy = deepcopy(env.mapMat)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.mapMat[i, j] == 1:
policy[i, j] = -1
else:
sensorInfo = env.agent.getSensorInfoFromPos(np.array([i, j, phi]))
distance = np.array(config['targetState']) - np.array([i, j])
dx = distance[0] * math.cos(phi) + distance[1] * math.sin(phi)
