dy = distance[0] * math.sin(
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)
policyNet = MultiLayerNetRegression(netParameter['n_feature'],
netParameter['n_hidden'],
netParameter['n_output'])
targetNet = deepcopy(policyNet)
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
agent = DQNAgent(policyNet,
targetNet,
env,
optimizer,
torch.nn.MSELoss(),
N_S,
N_A,
config=config)
policy = deepcopy(env.map)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.map[i, j] == 0:
policy[i, j] = -1
else:
policy[i, j] = agent.getPolicy(np.array([i, j]))
np.savetxt('DoubleQSimpleMazePolicyBeforeTrain' + mapName + '.txt',
policy,
fmt='%d',
delimiter='\t')
plotPolicy(policy, N_A)
agent.train()
policy = deepcopy(env.map)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.map[i, j] == 0:
policy[i, j] = -1
agent = DQNAgent(policyNet,
targetNet,
env,
optimizer,
torch.nn.MSELoss(),
N_A,
config=config)
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]))
policy[i, j] = agent.getPolicy(sensorInfo)
np.savetxt('DynamicMazePolicyBeforeTrain' + mapName + '.txt',
policy,
fmt='%d',
delimiter='\t')
#
# plotPolicy(policy, N_A)
agent.train()
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:
