phi) - distance[1] * math.cos(phi)
if math.sqrt(dx**2 +
dy**2) > agent.env.agent.targetClipLength:
angle = math.atan2(dy, dx)
dx = agent.env.agent.targetClipLength * math.cos(
angle)
dy = agent.env.agent.targetClipLength * math.sin(
angle)
#dx = agent.env.agent.targetClipMap(dx) if dx > 0 else -agent.env.agent.targetClipMap(-dx)
#dy = agent.env.agent.targetClipMap(dy) if dy > 0 else -agent.env.agent.targetClipMap(-dy)
state = {
'sensor': sensorInfo,
'target': np.array([dx, dy])
}
policy[i, j] = agent.getPolicy(state)
Qvalue = agent.policyNet(
stateProcessor([state], config['device'])[0])
value[i, j] = Qvalue[0, policy[i, j]].cpu().item()
np.savetxt('DynamicMazePolicyBeforeTrain' + config['mapName'] +
'phiIdx' + str(phiIdx) + '.txt',
policy,
fmt='%d',
delimiter='\t')
np.savetxt('DynamicMazeValueBeforeTrain' + config['mapName'] +
'phiIdx' + str(phiIdx) + '.txt',
value,
fmt='%.3f',
delimiter='\t')
# plotPolicy(policy, N_A)
agent.train()
agent.env.currentState = np.array([0.0, agent.env.mapWidth / 2 + 1])
state['state']= np.array([0.0, agent.env.mapWidth / 2 + 1])
for step in range(nSteps):
action = agent.select_action(agent.policyNet, state, -0.1)
nextState, reward, done, info = agent.env.step(action)
state = nextState
if done:
print('finish step:', agent.env.stepCount)
print(agent.env.currentState)
break
policyFlag = True
if policyFlag:
policy = np.zeros((env.mapHeight, env.mapWidth), dtype=np.int32)
value = np.zeros((env.mapHeight, env.mapWidth), dtype=np.float)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
state = {'state': np.array([i / agent.env.lengthScale, \
j / agent.env.lengthScale])
}
policy[i, j] = agent.select_action(agent.policyNet, state, -0.1)
stateTorch = stateProcessor([state], config['device'])[0]
Qvalue = agent.policyNet(stateTorch)
value[i, j] = Qvalue[0, policy[i, j]].cpu().item()
np.savetxt('SimpleMazePolicyAfterTrain_stage.txt', policy, fmt='%d', delimiter='\t')
np.savetxt('SimpleMazeValueAfterTrain_stage.txt', value, fmt='%f', delimiter='\t')
