def main():
np.set_printoptions(formatter={'float_kind': lambda x: "%.2f" % x})
# Define environment
env = envstandalone.BlockArrange()
# Dictionary-based value function
q_func_tabular = {}
# cols of vectorKey must be boolean less than 64 bits long
def getTabularKeys(vectorKey):
obsBits = np.packbits(vectorKey, 1)
obsKeys = 0
for i in range(np.shape(obsBits)[1]):
# IMPORTANT: the number of bits in the type cast below (UINT64) must be at least as big
# as the bits required to encode obsBits. If it is too small, we get hash collisions...
obsKeys = obsKeys + (256**i) * np.uint64(obsBits[:, i])
return obsKeys
def getTabular(vectorKey):
keys = getTabularKeys(vectorKey)
# return np.array([q_func[x] if x in q_func else 0*np.ones(num_states) for x in keys])
return np.array([
q_func_tabular[x] if x in q_func_tabular else 10 *
np.ones(num_states) for x in keys
])
def trainTabular(vectorKey, qCurrTargets, weights):
keys = getTabularKeys(vectorKey)
alpha = 0.2
for i in range(len(keys)):
if keys[i] in q_func_tabular:
# q_func[keys[i]] = (1-alpha)*q_func[keys[i]] + alpha*qCurrTargets[i]
q_func_tabular[keys[i]] = q_func_tabular[
keys[i]] + alpha * weights[i, :] * (
qCurrTargets[i] - q_func_tabular[keys[i]]
) # (1-alpha)*q_func[keys[i]] + alpha*qCurrTargets[i]
else:
q_func_tabular[keys[i]] = qCurrTargets[i]
# Standard DQN parameters
# max_timesteps=40000
max_timesteps = 10000
# max_timesteps=1000
learning_starts = 1000
# learning_starts=10
# buffer_size=50000
buffer_size = 10000
# buffer_size=1000
# buffer_size=320
# buffer_size=32
# buffer_size=8
# buffer_size=1
exploration_fraction = 0.2
exploration_final_eps = 0.02
print_freq = 1
# gamma=.98
gamma = .9
target_network_update_freq = 1
batch_size = 32
# batch_size=1
train_freq = 1
# train_freq=2
num_cpu = 16
# lr=0.001
lr = 0.0003
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
max_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
prioritized_replay = True
# prioritized_replay=False
# prioritized_replay_alpha=1.0
prioritized_replay_alpha = 0.6
prioritized_replay_beta0 = 0.4
prioritized_replay_beta_iters = None
# prioritized_replay_beta_iters=20000
prioritized_replay_eps = 1e-6
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = max_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
beta = 1
# Deictic state/action parameters
deicticShape = (3, 3, 2
) # IMPORTANT: first two elts of deicticShape must be odd
deicticActionShape = (3, 3, 4)
num_cascade = 5
num_states = env.num_blocks + 1 # one more state than blocks to account for not holding anything
num_patches = env.maxSide**2
num_actions = 2 * num_patches
num_actions_discrete = 2
# valueFunctionType = "TABULAR"
valueFunctionType = "DQN"
# ******* Build tensorflow functions ********
q_func = models.cnn_to_mlp(
# q_func = models.cnn_to_mlp_2pathways(
convs=[(32, 3, 1)],
hiddens=[32],
dueling=True)
def make_obs_ph(name):
return U.BatchInput(env.observation_space.spaces[0].shape, name=name)
def make_actionDeic_ph(name):
return U.BatchInput(deicticActionShape, name=name)
def make_target_ph(name):
# return U.BatchInput([num_actions], name=name)
# return U.BatchInput([num_cascade,num_states], name=name)
return U.BatchInput([num_states], name=name)
def make_weight_ph(name):
return U.BatchInput([num_states], name=name)
getMoveActionDescriptors = build_getMoveActionDescriptors(
make_obs_ph=make_obs_ph, deicticShape=deicticShape)
getq = build_getq(make_actionDeic_ph=make_actionDeic_ph,
q_func=q_func,
num_states=num_states,
num_cascade=num_cascade,
scope="deepq",
qscope="q_func")
targetTrain = build_targetTrain(
make_actionDeic_ph=make_actionDeic_ph,
make_target_ph=make_target_ph,
make_weight_ph=make_weight_ph,
q_func=q_func,
num_states=num_states,
num_cascade=num_cascade,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
# optimizer=tf.train.GradientDescentOptimizer(learning_rate=lr),
scope="deepq",
qscope="q_func",
grad_norm_clipping=1.
# grad_norm_clipping=0.1
)
# Start tensorflow session
sess = U.make_session(num_cpu)
sess.__enter__()
episode_rewards = [0.0]
timerStart = time.time()
U.initialize()
obs = env.reset()
for t in range(max_timesteps):
# Get state: in range(0,env.num_blocks)
stateDeictic = obs[1] # holding
# Get action set: <num_patches> pick actions followed by <num_patches> place actions
moveDescriptors = getMoveActionDescriptors([obs[0]])
actionsPickDescriptors = np.concatenate(
[moveDescriptors,
np.zeros(np.shape(moveDescriptors))], axis=3)
actionsPlaceDescriptors = np.concatenate(
[np.zeros(np.shape(moveDescriptors)), moveDescriptors], axis=3)
actionDescriptors = np.r_[actionsPickDescriptors,
actionsPlaceDescriptors]
if valueFunctionType == "TABULAR":
actionDescriptorsFlat = np.reshape(actionDescriptors, [
-1, deicticActionShape[0] * deicticActionShape[1] *
deicticActionShape[2]
]) == 1
qCurr = getTabular(actionDescriptorsFlat)
else:
qCurr = getq(actionDescriptors)
# select action
qCurrNoise = qCurr + np.random.random(np.shape(
qCurr)) * 0.01 # add small amount of noise to break ties randomly
action = np.argmax(qCurrNoise[:, stateDeictic])
if np.random.rand() < exploration.value(t):
action = np.random.randint(num_actions)
# take action
new_obs, rew, done, _ = env.step(action)
replay_buffer.add(stateDeictic, actionDescriptors[action, :], rew,
new_obs, float(done))
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
beta = beta_schedule.value(t)
states_t, actions, rewards, images_tp1, states_tp1, dones, weights, batch_idxes = replay_buffer.sample(
batch_size, beta)
else:
states_t, actions, rewards, images_tp1, states_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
moveDescriptorsNext1 = getMoveActionDescriptors(images_tp1)
actionsPickDescriptorsNext1 = np.concatenate([
moveDescriptorsNext1,
np.zeros(np.shape(moveDescriptorsNext1))
],
axis=3)
actionsPlaceDescriptorsNext1 = np.concatenate([
np.zeros(np.shape(moveDescriptorsNext1)), moveDescriptorsNext1
],
axis=3)
actionDescriptorsNext1 = np.stack(
[actionsPickDescriptorsNext1, actionsPlaceDescriptorsNext1],
axis=0)
actionDescriptorsNext1 = np.reshape(actionDescriptorsNext1, [
batch_size * num_patches * num_actions_discrete,
deicticActionShape[0], deicticActionShape[1],
deicticActionShape[2]
])
if valueFunctionType == "TABULAR":
actionDescriptorsNextFlat1 = np.reshape(
actionDescriptorsNext1,
[batch_size * num_patches * num_actions_discrete, -1]) == 1
qNextFlat1 = getTabular(actionDescriptorsNextFlat1)
else:
qNextFlat1 = getq(actionDescriptorsNext1)
qNext1 = np.reshape(
qNextFlat1,
[batch_size, num_patches, num_actions_discrete, num_states])
qNextmax1 = np.max(
np.max(qNext1[range(batch_size), :, :, states_tp1], 2), 1)
targets1 = rewards + (1 - dones) * gamma * qNextmax1
if valueFunctionType == "TABULAR":
actionsFlat = np.reshape(actions, [batch_size, -1]) == 1
qCurrTarget1 = getTabular(actionsFlat)
else:
qCurrTarget1 = getq(actions)
td_errors = qCurrTarget1[range(batch_size), states_t] - targets1
qCurrTarget1[range(batch_size), states_t] = targets1
if valueFunctionType == "TABULAR":
trainTabular(actionsFlat, qCurrTarget1,
np.transpose(np.tile(
weights, [num_states, 1]))) # (TABULAR)
else:
targetTrain(actions, qCurrTarget1,
np.transpose(np.tile(weights,
[num_states, 1]))) # (DQN)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
# bookkeeping for storing episode rewards
episode_rewards[-1] += rew
if done:
new_obs = env.reset()
episode_rewards.append(0.0)
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
timerFinal = time.time()
print("steps: " + str(t) + ", episodes: " + str(num_episodes) +
", mean 100 episode reward: " + str(mean_100ep_reward) +
", % time spent exploring: " +
str(int(100 * exploration.value(t))) + ", beta: " +
str(beta) + ", time elapsed: " +
str(timerFinal - timerStart))
timerStart = timerFinal
obs = new_obs
# display value function
obs = env.reset()
moveDescriptors = getMoveActionDescriptors([obs[0]])
actionsPickDescriptors = np.concatenate(
[moveDescriptors, np.zeros(np.shape(moveDescriptors))], axis=3)
actionsPlaceDescriptors = np.concatenate(
[np.zeros(np.shape(moveDescriptors)), moveDescriptors], axis=3)
print(str(obs[0][:, :, 0]))
qPick = getq(actionsPickDescriptors)
# qPick = getTabular(np.reshape(actionsPickDescriptors,[num_patches,-1])==1)
print("Value function for pick action in hold-nothing state:")
print(str(np.reshape(qPick[:, 0], [8, 8])))
qPlace = getq(actionsPlaceDescriptors)
# qPlace = getTabular(np.reshape(actionsPlaceDescriptors,[num_patches,-1])==1)
print("Value function for place action in hold-1 state:")
print(str(np.reshape(qPlace[:, 1], [8, 8])))
print("Value function for place action in hold-2 state:")
print(str(np.reshape(qPlace[:, 2], [8, 8])))
def main(envStride, fileIn, fileOut, inputmaxtimesteps):
reuseModels = None
np.set_printoptions(formatter={'float_kind':lambda x: "%.2f" % x})
env = envstandalone.PuckArrange()
env.stride = envStride # stride input to this problem
env.reset() # need to do the reset her in order to populate parameters
# Standard q-learning parameters
# max_timesteps=2000
max_timesteps=inputmaxtimesteps
exploration_fraction=0.3
exploration_final_eps=0.1
gamma=.90
num_cpu = 16
# Used by buffering and DQN
learning_starts=60
buffer_size=1000
batch_size=10
target_network_update_freq=1
train_freq=1
print_freq=1
lr=0.0003
# first two elts of deicticShape must be odd
descriptorShape = (env.blockSize*3,env.blockSize*3,2)
# descriptorShapeSmall = (10,10,2)
# descriptorShapeSmall = (15,15,2)
descriptorShapeSmall = (20,20,2)
num_states = 2 # either holding or not
num_patches = len(env.moveCenters)**2
num_actions = 2*num_patches
num_actions_discrete = 2
# valueFunctionType = "TABULAR"
valueFunctionType = "DQN"
# actionSelectionStrategy = "UNIFORM_RANDOM" # actions are selected randomly from collection of all actions
actionSelectionStrategy = "RANDOM_UNIQUE" # each unique action descriptor has equal chance of being selected
episode_rewards = [0.0]
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * max_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# prioritized_replay=True
prioritized_replay=False
# prioritized_replay_alpha=1.0
prioritized_replay_alpha=0.6
prioritized_replay_beta0=0.4
prioritized_replay_beta_iters=None
# prioritized_replay_beta_iters=20000
prioritized_replay_eps=1e-6
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = max_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
beta = 1
q_func = models.cnn_to_mlp(
# q_func = models.cnn_to_mlp_2pathways(
# convs=[(16,3,1), (32,3,1)],
# hiddens=[48],
convs=[(16,3,1)],
hiddens=[32],
# convs=[(32,3,1)],
# hiddens=[48],
# convs=[(48,3,1)],
# hiddens=[48],
dueling=True
)
def make_obs_ph(name):
return U.BatchInput(env.observation_space.spaces[0].shape, name=name)
def make_actionDeic_ph(name):
return U.BatchInput(descriptorShapeSmall, name=name)
def make_target_ph(name):
return U.BatchInput([1], name=name)
def make_weight_ph(name):
return U.BatchInput([1], name=name)
getMoveActionDescriptors = build_getMoveActionDescriptors(make_obs_ph=make_obs_ph,actionShape=descriptorShape,actionShapeSmall=descriptorShapeSmall,stride=env.stride)
if valueFunctionType == 'DQN':
getqNotHolding = build_getq(
make_actionDeic_ph=make_actionDeic_ph,
q_func=q_func,
num_states=num_states,
num_cascade=5,
scope="deepq",
qscope="q_func_notholding",
reuse=reuseModels
)
getqHolding = build_getq(
make_actionDeic_ph=make_actionDeic_ph,
q_func=q_func,
num_states=num_states,
num_cascade=5,
scope="deepq",
qscope="q_func_holding",
reuse=reuseModels
)
targetTrainNotHolding = build_targetTrain(
make_actionDeic_ph=make_actionDeic_ph,
make_target_ph=make_target_ph,
make_weight_ph=make_weight_ph,
q_func=q_func,
num_states=num_states,
num_cascade=5,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
scope="deepq",
qscope="q_func_notholding",
grad_norm_clipping=1.,
reuse=reuseModels
)
targetTrainHolding = build_targetTrain(
make_actionDeic_ph=make_actionDeic_ph,
make_target_ph=make_target_ph,
make_weight_ph=make_weight_ph,
q_func=q_func,
num_states=num_states,
num_cascade=5,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
scope="deepq",
qscope="q_func_holding",
grad_norm_clipping=1.,
reuse=reuseModels
)
sess = U.make_session(num_cpu)
sess.__enter__()
obs = env.reset()
episode_rewards = [0.0]
td_errors = [0.0]
timerStart = time.time()
U.initialize()
# load prior model
if fileIn != "None":
saver = tf.train.Saver()
saver.restore(sess, fileIn)
for t in range(max_timesteps):
# Get action set: <num_patches> pick actions followed by <num_patches> place actions
moveDescriptors = getMoveActionDescriptors([obs[0]])
moveDescriptors = moveDescriptors*2-1
actionsPickDescriptors = np.stack([moveDescriptors, np.zeros(np.shape(moveDescriptors))],axis=3)
actionsPlaceDescriptors = np.stack([np.zeros(np.shape(moveDescriptors)),moveDescriptors],axis=3)
actionDescriptors = np.r_[actionsPickDescriptors,actionsPlaceDescriptors]
qCurrNotHolding = getqNotHolding(actionDescriptors)
qCurrHolding = getqHolding(actionDescriptors)
qCurr = np.concatenate([qCurrNotHolding,qCurrHolding],axis=1)
# select action at random
qCurrNoise = qCurr + np.random.random(np.shape(qCurr))*0.01 # add small amount of noise to break ties randomly
if actionSelectionStrategy == "UNIFORM_RANDOM":
action = np.argmax(qCurrNoise[:,obs[1]])
if np.random.rand() < exploration.value(t):
action = np.random.randint(num_actions)
elif actionSelectionStrategy == "RANDOM_UNIQUE":
_,idx,inv = np.unique(actionDescriptors,axis=0,return_index=True,return_inverse=True)
actionIdx = np.argmax(qCurrNoise[idx,obs[1]])
if np.random.rand() < exploration.value(t):
actionIdx = np.random.randint(len(idx))
actionsSelected = np.nonzero(inv==actionIdx)[0]
action = actionsSelected[np.random.randint(len(actionsSelected))]
else:
print("Error...")
# take action
new_obs, rew, done, _ = env.step(action)
replay_buffer.add(obs[1], actionDescriptors[action,:], rew, np.copy(new_obs), float(done))
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
beta=beta_schedule.value(t)
states_t, actionPatches, rewards, images_tp1, states_tp1, dones, weights, batch_idxes = replay_buffer.sample(batch_size, beta)
else:
states_t, actionPatches, rewards, images_tp1, states_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
moveDescriptorsNext = getMoveActionDescriptors(images_tp1)
moveDescriptorsNext = moveDescriptorsNext*2-1
actionsPickDescriptorsNext = np.stack([moveDescriptorsNext, np.zeros(np.shape(moveDescriptorsNext))],axis=3)
actionsPlaceDescriptorsNext = np.stack([np.zeros(np.shape(moveDescriptorsNext)), moveDescriptorsNext],axis=3)
actionDescriptorsNext = np.stack([actionsPickDescriptorsNext, actionsPlaceDescriptorsNext], axis=1) # I sometimes get this axis parameter wrong... pay attention!
actionDescriptorsNext = np.reshape(actionDescriptorsNext,[-1,descriptorShapeSmall[0],descriptorShapeSmall[1],descriptorShapeSmall[2]])
qNextNotHolding = getqNotHolding(actionDescriptorsNext)
qNextHolding = getqHolding(actionDescriptorsNext)
qNextFlat = np.concatenate([qNextNotHolding,qNextHolding],axis=1)
qNext = np.reshape(qNextFlat,[batch_size,num_patches,num_actions_discrete,num_states])
qNextmax = np.max(np.max(qNext[range(batch_size),:,:,states_tp1],2),1)
targets = rewards + (1-dones) * gamma * qNextmax
qCurrTargetNotHolding = getqNotHolding(actionPatches)
qCurrTargetHolding = getqHolding(actionPatches)
qCurrTarget = np.concatenate([qCurrTargetNotHolding,qCurrTargetHolding],axis=1)
td_error = qCurrTarget[range(batch_size),states_t] - targets
qCurrTarget[range(batch_size),states_t] = targets
targetTrainNotHolding(actionPatches, np.reshape(qCurrTarget[:,0],[batch_size,1]), np.reshape(weights,[batch_size,1]))
targetTrainHolding(actionPatches, np.reshape(qCurrTarget[:,1],[batch_size,1]), np.reshape(weights,[batch_size,1]))
if prioritized_replay:
new_priorities = np.abs(td_error) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
td_errors[-1] += td_error
# bookkeeping for storing episode rewards
episode_rewards[-1] += rew
if done:
new_obs = env.reset()
episode_rewards.append(0.0)
td_errors.append(0.0)
mean_100ep_reward = round(np.mean(episode_rewards[-51:-1]), 1)
# mean_100ep_tderror = round(np.mean(td_errors[-51:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
timerFinal = time.time()
# print("steps: " + str(t) + ", episodes: " + str(num_episodes) + ", mean 100 episode reward: " + str(mean_100ep_reward) + ", % time spent exploring: " + str(int(100 * exploration.value(t))) + ", time elapsed: " + str(timerFinal - timerStart) + ", tderror: " + str(mean_100ep_tderror))
print("steps: " + str(t) + ", episodes: " + str(num_episodes) + ", mean 100 episode reward: " + str(mean_100ep_reward) + ", % time spent exploring: " + str(int(100 * exploration.value(t))) + ", time elapsed: " + str(timerFinal - timerStart))
timerStart = timerFinal
obs = np.copy(new_obs)
# save what we learned
if fileOut != "None":
saver = tf.train.Saver()
saver.save(sess, fileOut)
# display value function
obs = env.reset()
moveDescriptors = getMoveActionDescriptors([obs[0]])
moveDescriptors = moveDescriptors*2-1
gridSize = np.int32(np.sqrt(np.shape(moveDescriptors)[0]))
actionsPickDescriptors = np.stack([moveDescriptors, np.zeros(np.shape(moveDescriptors))],axis=3)
actionsPlaceDescriptors = np.stack([np.zeros(np.shape(moveDescriptors)), moveDescriptors],axis=3)
print(str(obs[0][:,:,0]))
qPickNotHolding = getqNotHolding(actionsPickDescriptors)
qPickHolding = getqHolding(actionsPickDescriptors)
qPick = np.concatenate([qPickNotHolding,qPickHolding],axis=1)
print("Value function for pick action in hold-nothing state:")
print(str(np.reshape(qPick[:,0],[gridSize,gridSize])))
print("Value function for pick action in hold-1 state:")
print(str(np.reshape(qPick[:,1],[gridSize,gridSize])))
qPlaceNotHolding = getqNotHolding(actionsPlaceDescriptors)
qPlaceHolding = getqHolding(actionsPlaceDescriptors)
qPlace = np.concatenate([qPlaceNotHolding,qPlaceHolding],axis=1)
print("Value function for place action in hold-nothing state:")
print(str(np.reshape(qPlace[:,0],[gridSize,gridSize])))
print("Value function for place action in hold-1 state:")
print(str(np.reshape(qPlace[:,1],[gridSize,gridSize])))
plt.subplot(1,3,1)
plt.imshow(np.tile(env.state[0],[1,1,3]))
plt.subplot(1,3,2)
plt.imshow(np.reshape(qPick[:,0],[gridSize,gridSize]))
plt.subplot(1,3,3)
plt.imshow(np.reshape(qPlace[:,1],[gridSize,gridSize]))
plt.show()