def __init__(self,
environment,
learning_algo,
replay_memory_size=1000000,
replay_start_size=None,
batch_size=32,
random_state=np.random.RandomState(),
exp_priority=0,
train_policy=None,
test_policy=None,
only_full_history=True):
inputDims = environment.inputDimensions()
if replay_start_size == None:
replay_start_size = max(inputDims[i][0]
for i in range(len(inputDims)))
elif replay_start_size < max(inputDims[i][0]
for i in range(len(inputDims))):
raise AgentError(
"Replay_start_size should be greater than the biggest history of a state."
)
self._controllers = []
self._environment = environment
self._learning_algo = learning_algo
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size
self._batch_size = batch_size
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
self._dataset = DataSet(environment,
max_size=replay_memory_size,
random_state=random_state,
use_priority=self._exp_priority,
only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=float))
if (train_policy == None):
self._train_policy = EpsilonGreedyPolicy(learning_algo,
environment.nActions(),
random_state, 0.1)
else:
self._train_policy = train_policy
if (test_policy == None):
self._test_policy = EpsilonGreedyPolicy(learning_algo,
environment.nActions(),
random_state, 0.)
else:
self._test_policy = test_policy
self.gathering_data = True # Whether the agent is gathering data or not
self.sticky_action = 1 # Number of times the agent is forced to take the same action as part of one actual time step
def __init__(self,
environment,
q_network,
replay_memory_size=1000000,
replay_start_size=None,
batch_size=32,
random_state=np.random.RandomState(),
exp_priority=0,
train_policy=None,
test_policy=None,
only_full_history=True):
inputDims = environment.inputDimensions()
if replay_start_size == None:
replay_start_size = max(inputDims[i][0]
for i in range(len(inputDims)))
elif replay_start_size < max(inputDims[i][0]
for i in range(len(inputDims))):
raise AgentError(
"Replay_start_size should be greater than the biggest history of a state."
)
self._controllers = []
self._environment = environment
self._network = q_network
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size
self._batch_size = batch_size
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
self._dataset = DataSet(environment,
max_size=replay_memory_size,
random_state=random_state,
use_priority=self._exp_priority,
only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._mode_epochs_length = 0
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=config.floatX))
if (train_policy == None):
self._train_policy = EpsilonGreedyPolicy(q_network,
environment.nActions(),
random_state, 0.1)
else:
self._train_policy = train_policy
if (test_policy == None):
self._test_policy = EpsilonGreedyPolicy(q_network,
environment.nActions(),
random_state, 0.)
else:
self._test_policy = test_policy
def __init__(self, environment, q_network, replay_memory_size=1000000, replay_start_size=None, batch_size=32, random_state=np.random.RandomState(), exp_priority=0, train_policy=None, test_policy=None, only_full_history=True):
inputDims = environment.inputDimensions()
if replay_start_size == None:
replay_start_size = max(inputDims[i][0] for i in range(len(inputDims)))
elif replay_start_size < max(inputDims[i][0] for i in range(len(inputDims))) :
raise AgentError("Replay_start_size should be greater than the biggest history of a state.")
self._controllers = []
self._environment = environment
self._network = q_network
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size
self._batch_size = batch_size
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
self._dataset = DataSet(environment, max_size=replay_memory_size, random_state=random_state, use_priority=self._exp_priority, only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._mode_epochs_length = 0
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=config.floatX))
if (train_policy==None):
self._train_policy = EpsilonGreedyPolicy(q_network, environment.nActions(), random_state, 0.1)
else:
self._train_policy = train_policy
if (test_policy==None):
self._test_policy = EpsilonGreedyPolicy(q_network, environment.nActions(), random_state, 0.)
else:
self._test_policy = test_policy
class NeuralAgent(object):
"""The NeuralAgent class wraps a deep Q-network for training and testing in a given environment.
Attach controllers to it in order to conduct an experiment (when to train the agent, when to test,...).
Parameters
-----------
environment : object from class Environment
The environment in which the agent interacts
q_network : object from class QNetwork
The q_network associated to the agent
replay_memory_size : int
Size of the replay memory. Default : 1000000
replay_start_size : int
Number of observations (=number of time steps taken) in the replay memory before starting learning. Default: minimum possible according to environment.inputDimensions().
batch_size : int
Number of tuples taken into account for each iteration of gradient descent. Default : 32
random_state : numpy random number generator
Default : random seed.
exp_priority : float
The exponent that determines how much prioritization is used, default is 0 (uniform priority).
One may check out Schaul et al. (2016) - Prioritized Experience Replay.
"""
def __init__(self, environment, q_network, replay_memory_size=1000000, replay_start_size=None, batch_size=32, random_state=np.random.RandomState(), exp_priority=0, train_policy=None, test_policy=None):
inputDims = environment.inputDimensions()
if replay_start_size == None:
replay_start_size = max(inputDims[i][0] for i in range(len(inputDims)))
elif replay_start_size < max(inputDims[i][0] for i in range(len(inputDims))) :
raise AgentError("Replay_start_size should be greater than the biggest history of a state.")
self._controllers = []
self._environment = environment
self._network = q_network
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size
self._batch_size = batch_size
self._random_state = random_state
self._exp_priority = exp_priority
self._dataset = DataSet(environment, max_size=replay_memory_size, random_state=random_state, use_priority=self._exp_priority)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._mode_epochs_length = 0
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=config.floatX))
if (train_policy==None):
self._train_policy = EpsilonGreedyPolicy(q_network, environment.nActions(), random_state, 0.1)
else:
self._train_policy = train_policy
if (test_policy==None):
self._test_policy = EpsilonGreedyPolicy(q_network, environment.nActions(), random_state, 0.)
else:
self._test_policy = test_policy
def setControllersActive(self, toDisable, active):
""" Activate controller
"""
for i in toDisable:
self._controllers[i].setActive(active)
def setLearningRate(self, lr):
""" Set the learning rate for the gradient descent
"""
self._network.setLearningRate(lr)
def learningRate(self):
""" Get the learning rate
"""
return self._network.learningRate()
def setDiscountFactor(self, df):
""" Set the discount factor
"""
self._network.setDiscountFactor(df)
def discountFactor(self):
""" Get the discount factor
"""
return self._network.discountFactor()
def overrideNextAction(self, action):
""" Possibility to override the chosen action. This possibility should be used on the signal OnActionChosen.
"""
self._selected_action = action
def avgBellmanResidual(self):
""" Returns the average training loss on the epoch
"""
if (len(self._training_loss_averages) == 0):
return -1
return np.average(self._training_loss_averages)
def avgEpisodeVValue(self):
""" Returns the average V value on the episode
"""
if (len(self._Vs_on_last_episode) == 0):
return -1
return np.average(self._Vs_on_last_episode)
def totalRewardOverLastTest(self):
""" Returns the average sum of reward per episode
"""
return self._total_mode_reward/self._totalModeNbrEpisode
def bestAction(self):
""" Returns the best Action
"""
action = self._network.chooseBestAction(self._state)
V = max(self._network.qValues(self._state))
return action, V
def attach(self, controller):
if (isinstance(controller, controllers.Controller)):
self._controllers.append(controller)
else:
raise TypeError("The object you try to attach is not a Controller.")
def detach(self, controllerIdx):
return self._controllers.pop(controllerIdx)
def mode(self):
return self._mode
def startMode(self, mode, epochLength):
if self._in_episode:
raise AgentError("Trying to start mode while current episode is not yet finished. This method can be "
"called only *between* episodes for testing and validation.")
elif mode == -1:
raise AgentError("Mode -1 is reserved and means 'training mode'; use resumeTrainingMode() instead.")
else:
self._mode = mode
self._mode_epochs_length = epochLength
self._total_mode_reward = 0
del self._tmp_dataset
self._tmp_dataset = DataSet(self._environment, self._random_state, max_size=self._replay_memory_size)
def resumeTrainingMode(self):
self._mode = -1
def summarizeTestPerformance(self):
if self._mode == -1:
raise AgentError("Cannot summarize test performance outside test environment.")
self._environment.summarizePerformance(self._tmp_dataset)
def train(self):
if self._dataset.n_elems < self._replay_start_size:
return
try:
states, actions, rewards, next_states, terminals, rndValidIndices = self._dataset.randomBatch(self._batch_size, self._exp_priority)
loss, loss_ind = self._network.train(states, actions, rewards, next_states, terminals)
self._training_loss_averages.append(loss)
if (self._exp_priority):
self._dataset.updatePriorities(pow(loss_ind,self._exp_priority)+0.0001, rndValidIndices[1])
except SliceError as e:
warn("Training not done - " + str(e), AgentWarning)
def dumpNetwork(self, fname, nEpoch=-1):
""" Dump the network
Parameters
-----------
fname : string
Name of the file where the network will be dumped
nEpoch : int
Epoch number (Optional)
"""
try:
os.mkdir("nnets")
except Exception:
pass
basename = "nnets/" + fname
for f in os.listdir("nnets/"):
if fname in f:
os.remove("nnets/" + f)
all_params = self._network.getAllParams()
if (nEpoch>=0):
joblib.dump(all_params, basename + ".epoch={}".format(nEpoch))
else:
joblib.dump(all_params, basename, compress=True)
def setNetwork(self, fname, nEpoch=-1):
""" Set values into the network
Parameters
-----------
fname : string
Name of the file where the values are
nEpoch : int
Epoch number (Optional)
"""
basename = "nnets/" + fname
if (nEpoch>=0):
all_params = joblib.load(basename + ".epoch={}".format(nEpoch))
else:
all_params = joblib.load(basename)
self._network.setAllParams(all_params)
def run(self, n_epochs, epoch_length):
for c in self._controllers: c.onStart(self)
i = 0
while i < n_epochs or self._mode_epochs_length > 0:
self._training_loss_averages = []
if self._mode != -1:
self._totalModeNbrEpisode=0
while self._mode_epochs_length > 0:
self._totalModeNbrEpisode += 1
self._mode_epochs_length = self._runEpisode(self._mode_epochs_length)
else:
length = epoch_length
while length > 0:
length = self._runEpisode(length)
i += 1
for c in self._controllers: c.onEpochEnd(self)
self._environment.end()
for c in self._controllers: c.onEnd(self)
def _runEpisode(self, maxSteps):
self._in_episode = True
initState = self._environment.reset(self._mode)
inputDims = self._environment.inputDimensions()
for i in range(len(inputDims)):
if inputDims[i][0] > 1:
self._state[i][1:] = initState[i][1:]
self._Vs_on_last_episode = []
while maxSteps > 0:
maxSteps -= 1
obs = self._environment.observe()
is_terminal = self._environment.inTerminalState()
if (is_terminal==True):
action=0
reward=0
else:
for i in range(len(obs)):
self._state[i][0:-1] = self._state[i][1:]
self._state[i][-1] = obs[i]
V, action, reward = self._step()
self._Vs_on_last_episode.append(V)
if self._mode != -1:
self._total_mode_reward += reward
self._addSample(obs, action, reward, is_terminal)
for c in self._controllers: c.onActionTaken(self)
if is_terminal:
break
self._in_episode = False
for c in self._controllers: c.onEpisodeEnd(self, is_terminal, reward)
return maxSteps
def _step(self):
"""
This method is called at each time step. If the agent is currently in testing mode, and if its *test* replay
memory has enough samples, it will select the best action it can. If there are not enough samples, FIXME.
In the case the agent is not in testing mode, if its replay memory has enough samples, it will select the best
action it can with probability 1-CurrentEpsilon and a random action otherwise. If there are not enough samples,
it will always select a random action.
Parameters
-----------
state : ndarray
An ndarray(size=number_of_inputs, dtype='object), where states[input] is a 1+D matrix of dimensions
input.historySize x "shape of a given ponctual observation for this input".
Returns
-------
action : int
The id of the action selected by the agent.
V : float
Estimated value function of current state.
"""
action, V = self._chooseAction()
reward = self._environment.act(action)
return V, action, reward
def _addSample(self, ponctualObs, action, reward, is_terminal):
if self._mode != -1:
self._tmp_dataset.addSample(ponctualObs, action, reward, is_terminal, priority=1)
else:
self._dataset.addSample(ponctualObs, action, reward, is_terminal, priority=1)
def _chooseAction(self):
if self._mode != -1:
# Act according to the test policy if not in training mode
action, V = self._test_policy.act(self._state)
else:
if self._dataset.n_elems > self._replay_start_size:
# follow the train policy
action, V = self._train_policy.act(self._state) #is self._state the only way to store/pass the state?
else:
# Still gathering initial data: choose dummy action
action = self._random_state.randint(0, self._environment.nActions())
V = 0
for c in self._controllers: c.onActionChosen(self, action)
return action, V
# --- Instantiate learning_algo ---
learning_algo = CRAR(env,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_norm,
parameters.freeze_interval,
parameters.batch_size,
parameters.update_rule,
rng,
double_Q=True,
high_int_dim=HIGH_INT_DIM,
internal_dim=3)
train_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 1.)
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 0.1)
# --- Instantiate agent ---
agent = NeuralAgent(env,
learning_algo,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
train_policy=train_policy,
test_policy=test_policy)
# --- Create unique filename for FindBestController ---
h = hash(vars(parameters), hash_name="sha1")
**vars(parameters))
if parameters.action_type == 'q_argmax':
test_policy = ep.QArgmaxPolicy(learning_algo, env.nActions(), rng, parameters.epsilon_start)
train_policy = ep.QArgmaxPolicy(learning_algo, env.nActions(), rng, parameters.epsilon_start)
elif parameters.action_type == 'd_step_q_planning':
test_policy = ep.MCPolicy(learning_algo, parameters.reward_type, env.nActions(), rng, depth=parameters.depth, epsilon_start=parameters.epsilon_start)
train_policy = ep.MCPolicy(learning_algo, parameters.reward_type, env.nActions(), rng, depth=parameters.depth, epsilon_start=parameters.epsilon_start)
elif parameters.action_type == 'bootstrap_q':
test_policy = ep.BootstrapDQNPolicy(learning_algo, env.nActions(), rng, parameters.epsilon_start)
train_policy = ep.BootstrapDQNPolicy(learning_algo, env.nActions(), rng, parameters.epsilon_start)
elif parameters.action_type == 'd_step_reward_planning':
test_policy = ep.MCRewardPolicy(learning_algo, parameters.reward_type, env.nActions(), rng, depth=parameters.depth, epsilon_start=parameters.epsilon_start)
train_policy = ep.MCRewardPolicy(learning_algo, parameters.reward_type, env.nActions(), rng, depth=parameters.depth, epsilon_start=parameters.epsilon_start)
else:
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, parameters.epsilon_start)
train_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, parameters.epsilon_start)
# --- Instantiate agent ---
# We might need to change this.
train_q = parameters.action_type != 'd_step_reward_planning'
agent = SEAgent(
env,
learning_algo,
plotter,
random_state=rng,
train_policy=train_policy,
test_policy=test_policy,
train_q=train_q,
**vars(parameters))
"value": rng.randint(9999)
}, {
"key": "color_averaging",
"value": True
}, {
"key": "repeat_action_probability",
"value": 0.
}])
# --- Instantiate qnetwork ---
qnetwork = MyQNetwork(env, parameters.rms_decay, parameters.rms_epsilon,
parameters.momentum, parameters.clip_delta,
parameters.freeze_interval, parameters.batch_size,
parameters.update_rule, rng)
test_policy = EpsilonGreedyPolicy(qnetwork, env.nActions(), rng, 0.05)
# --- Instantiate agent ---
agent = NeuralAgent(env,
qnetwork,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
test_policy=test_policy)
# --- Create unique filename for FindBestController ---
h = hash(vars(parameters), hash_name="sha1")
fname = "ALE_" + h
print("The parameters hash is: {}".format(h))
from deer.policies import EpsilonGreedyPolicy
rng = np.random.RandomState(123456)
# TODO : best algorithm, hyperparameter tuning
if args.network == 'DQN':
network = MyQNetwork(environment=env,
batch_size=32,
double_Q=True,
random_state=rng)
elif args.network == 'DDPG':
network = MyACNetwork(environment=env, batch_size=32, random_state=rng)
agent = NeuralAgent(env,
network,
train_policy=EpsilonGreedyPolicy(network, env.nActions(),
rng, 0.0),
replay_memory_size=1000,
batch_size=32,
random_state=rng)
#agent.attach(bc.VerboseController())
if args.fname == 'baseline':
agent = EmpiricalTreatmentAgent(env)
else:
agent.setNetwork(args.fname)
count = 0
length_success = []
avg_rad = []
avg_h_cell_killed = []
avg_percentage = []
args.learning_rate[2]))
agent.attach(
bc.InterleavedTestEpochController(epoch_length=1000,
controllers_to_disable=[1, 2, 3, 4]))
elif args.network == 'DDPG':
network = MyACNetwork(environment=env,
batch_size=32,
double_Q=True,
freeze_interval=args.epochs[1],
random_state=rng)
agent = NeuralAgent(
env,
network,
train_policy=GaussianNoiseExplorationPolicy(
network, env.nActions(), rng, .5) if args.exploration == 'gauss'
else EpsilonGreedyPolicy(network, env.nActions(), rng, 0.1),
replay_memory_size=min(args.epochs[0] * args.epochs[1] * 2, 100000),
batch_size=32,
random_state=rng)
agent.setDiscountFactor(0.95)
agent.attach(bc.FindBestController(validationID=0,
unique_fname=args.fname))
agent.attach(bc.VerboseController())
agent.attach(bc.TrainerController())
if args.exploration == 'gauss':
agent.attach(
GaussianNoiseController(initial_std_dev=0.5,
n_decays=args.epochs[0] * args.epochs[1],
final_std_dev=0.005))
else:
agent.attach(
class NeuralAgent(object):
"""The NeuralAgent class wraps a learning algorithm (such as a deep Q-network) for training and testing in a given environment.
Attach controllers to it in order to conduct an experiment (when to train the agent, when to test,...).
Parameters
-----------
environment : object from class Environment
The environment in which the agent interacts
learning_algo : object from class LearningAlgo
The learning algorithm associated to the agent
replay_memory_size : int
Size of the replay memory. Default : 1000000
replay_start_size : int
Number of observations (=number of time steps taken) in the replay memory before starting learning.
Default: minimum possible according to environment.inputDimensions().
batch_size : int
Number of tuples taken into account for each iteration of gradient descent. Default : 32
random_state : numpy random number generator
Default : random seed.
exp_priority : float
The exponent that determines how much prioritization is used, default is 0 (uniform priority).
One may check out Schaul et al. (2016) - Prioritized Experience Replay.
train_policy : object from class Policy
Policy followed when in training mode (mode -1)
test_policy : object from class Policy
Policy followed when in other modes than training (validation and test modes)
only_full_history : boolean
Whether we wish to train the neural network only on full histories or we wish to fill with zeroes the
observations before the beginning of the episode
"""
def __init__(self,
environment,
learning_algo,
replay_memory_size=1000000,
replay_start_size=None,
batch_size=32,
random_state=np.random.RandomState(),
exp_priority=0,
train_policy=None,
test_policy=None,
only_full_history=True):
inputDims = environment.inputDimensions()
if replay_start_size == None:
replay_start_size = max(inputDims[i][0]
for i in range(len(inputDims)))
elif replay_start_size < max(inputDims[i][0]
for i in range(len(inputDims))):
raise AgentError(
"Replay_start_size should be greater than the biggest history of a state."
)
self._controllers = []
self._environment = environment
self._learning_algo = learning_algo
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size
self._batch_size = batch_size
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
self._dataset = DataSet(environment,
max_size=replay_memory_size,
random_state=random_state,
use_priority=self._exp_priority,
only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=float))
if (train_policy == None):
self._train_policy = EpsilonGreedyPolicy(learning_algo,
environment.nActions(),
random_state, 0.1)
else:
self._train_policy = train_policy
if (test_policy == None):
self._test_policy = EpsilonGreedyPolicy(learning_algo,
environment.nActions(),
random_state, 0.)
else:
self._test_policy = test_policy
self.gathering_data = True # Whether the agent is gathering data or not
self.sticky_action = 1 # Number of times the agent is forced to take the same action as part of one actual time step
def setControllersActive(self, toDisable, active):
""" Activate controller
"""
for i in toDisable:
self._controllers[i].setActive(active)
def setLearningRate(self, lr):
""" Set the learning rate for the gradient descent
"""
self._learning_algo.setLearningRate(lr)
def learningRate(self):
""" Get the learning rate
"""
return self._learning_algo.learningRate()
def setDiscountFactor(self, df):
""" Set the discount factor
"""
self._learning_algo.setDiscountFactor(df)
def discountFactor(self):
""" Get the discount factor
"""
return self._learning_algo.discountFactor()
def overrideNextAction(self, action):
""" Possibility to override the chosen action. This possibility should be used on the signal OnActionChosen.
"""
self._selected_action = action
def avgBellmanResidual(self):
""" Returns the average training loss on the epoch
"""
if (len(self._training_loss_averages) == 0):
return -1
return np.average(self._training_loss_averages)
def avgEpisodeVValue(self):
""" Returns the average V value on the episode (on time steps where a non-random action has been taken)
"""
if (len(self._Vs_on_last_episode) == 0):
return -1
if (np.trim_zeros(self._Vs_on_last_episode) != []):
return np.average(np.trim_zeros(self._Vs_on_last_episode))
else:
return 0
def totalRewardOverLastTest(self):
""" Returns the average sum of rewards per episode and the number of episode
"""
return self._total_mode_reward / self._totalModeNbrEpisode, self._totalModeNbrEpisode
def attach(self, controller):
if (isinstance(controller, controllers.Controller)):
self._controllers.append(controller)
else:
raise TypeError(
"The object you try to attach is not a Controller.")
def detach(self, controllerIdx):
return self._controllers.pop(controllerIdx)
def mode(self):
return self._mode
def startMode(self, mode, epochLength):
if self._in_episode:
raise AgentError(
"Trying to start mode while current episode is not yet finished. This method can be "
"called only *between* episodes for testing and validation.")
elif mode == -1:
raise AgentError(
"Mode -1 is reserved and means 'training mode'; use resumeTrainingMode() instead."
)
else:
self._mode = mode
self._total_mode_reward = 0.
del self._tmp_dataset
self._tmp_dataset = DataSet(
self._environment,
self._random_state,
max_size=self._replay_memory_size,
only_full_history=self._only_full_history)
def resumeTrainingMode(self):
self._mode = -1
def summarizeTestPerformance(self):
if self._mode == -1:
raise AgentError(
"Cannot summarize test performance outside test environment.")
self._environment.summarizePerformance(self._tmp_dataset,
self._learning_algo,
train_data_set=self._dataset)
def train(self):
"""
This function selects a random batch of data (with self._dataset.randomBatch) and performs a
Q-learning iteration (with self._learning_algo.train).
"""
# We make sure that the number of elements in the replay memory
# is strictly superior to self._replay_start_size before taking
# a random batch and perform training
if self._dataset.n_elems <= self._replay_start_size:
return
try:
if hasattr(self._learning_algo, 'nstep'):
observations, actions, rewards, terminals, rndValidIndices = self._dataset.randomBatch_nstep(
self._batch_size, self._learning_algo.nstep,
self._exp_priority)
loss, loss_ind = self._learning_algo.train(
observations, actions, rewards, terminals)
else:
states, actions, rewards, next_states, terminals, rndValidIndices = self._dataset.randomBatch(
self._batch_size, self._exp_priority)
loss, loss_ind = self._learning_algo.train(
states, actions, rewards, next_states, terminals)
self._training_loss_averages.append(loss)
if (self._exp_priority):
self._dataset.updatePriorities(
pow(loss_ind, self._exp_priority) + 0.0001,
rndValidIndices[1])
except SliceError as e:
warn("Training not done - " + str(e), AgentWarning)
def dumpNetwork(self, fname, nEpoch=-1):
""" Dump the network
Parameters
-----------
fname : string
Name of the file where the network will be dumped
nEpoch : int
Epoch number (Optional)
"""
try:
os.mkdir("nnets")
except Exception:
pass
basename = "nnets/" + fname
for f in os.listdir("nnets/"):
if fname in f:
os.remove("nnets/" + f)
all_params = self._learning_algo.getAllParams()
if (nEpoch >= 0):
joblib.dump(all_params, basename + ".epoch={}".format(nEpoch))
else:
joblib.dump(all_params, basename, compress=True)
def setNetwork(self, fname, nEpoch=-1):
""" Set values into the network
Parameters
-----------
fname : string
Name of the file where the values are
nEpoch : int
Epoch number (Optional)
"""
basename = "nnets/" + fname
if (nEpoch >= 0):
all_params = joblib.load(basename + ".epoch={}".format(nEpoch))
else:
all_params = joblib.load(basename)
self._learning_algo.setAllParams(all_params)
def run(self, n_epochs, epoch_length):
"""
This function encapsulates the inference and the learning.
If the agent is in train mode (mode = -1):
It starts by calling the controllers method "onStart",
Then it runs a given number of epochs where an epoch is made up of one or many episodes (called with
agent._runEpisode) and where an epoch ends up after the number of steps reaches the argument "epoch_length".
It ends up by calling the controllers method "end".
If the agent is on non train mode (mode > -1):
This function runs a number of epochs in non train mode (mode > -1), thus without controllers.
Parameters
-----------
n_epochs : int
number of epochs
epoch_length : int
maximum number of steps for a given epoch
"""
if (self._mode == -1):
self._run_train(n_epochs, epoch_length)
else:
self._run_non_train(n_epochs, epoch_length)
def _run_train(self, n_epochs, epoch_length):
"""
This function encapsulates the whole process of the learning.
It starts by calling the controllers method "onStart",
Then it runs a given number of epochs where an epoch is made up of one or many episodes (called with
agent._runEpisode) and where an epoch ends up after the number of steps reaches the argument "epoch_length".
It ends up by calling the controllers method "end".
Parameters
-----------
n_epochs : int
number of epochs
epoch_length : int
maximum number of steps for a given epoch
"""
for c in self._controllers:
c.onStart(self)
i = 0
while i < n_epochs:
self._training_loss_averages = []
while epoch_length > 0: # run new episodes until the number of steps left for the epoch has reached 0
epoch_length = self._runEpisode(epoch_length)
i += 1
for c in self._controllers:
c.onEpochEnd(self)
self._environment.end()
for c in self._controllers:
c.onEnd(self)
def _run_non_train(self, n_epochs, epoch_length):
"""
This function runs a number of epochs in non train mode (id > -1).
Parameters
-----------
n_epochs : int
number of epochs
epoch_length : int
maximum number of steps for a given epoch
"""
for c in self._controllers:
c.onStart(self)
i = 0
while i < n_epochs:
self._totalModeNbrEpisode = 0
while epoch_length > 0:
self._totalModeNbrEpisode += 1
epoch_length = self._runEpisode(epoch_length)
i += 1
for c in self._controllers:
c.onEpochEnd(self)
self._environment.end()
for c in self._controllers:
c.onEnd(self)
def _runEpisode(self, maxSteps):
"""
This function runs an episode of learning. An episode ends up when the environment method "inTerminalState"
returns True (or when the number of steps reaches the argument "maxSteps")
Parameters
-----------
maxSteps : int
maximum number of steps before automatically ending the episode
"""
self._in_episode = True
initState = self._environment.reset(self._mode)
inputDims = self._environment.inputDimensions()
for i in range(len(inputDims)):
if inputDims[i][0] > 1:
self._state[i][1:] = initState[i][1:]
self._Vs_on_last_episode = []
is_terminal = False
reward = 0
while maxSteps > 0:
maxSteps -= 1
if (self.gathering_data == True or self._mode != -1):
obs = self._environment.observe()
for i in range(len(obs)):
self._state[i][0:-1] = self._state[i][1:]
self._state[i][-1] = obs[i]
V, action, reward = self._step()
self._Vs_on_last_episode.append(V)
if self._mode != -1:
self._total_mode_reward += reward
is_terminal = self._environment.inTerminalState(
) # If the transition ends up in a terminal state, mark transition as terminal
# Note that the new obs will not be stored, as it is unnecessary.
if (maxSteps > 0):
self._addSample(obs, action, reward, is_terminal)
else:
self._addSample(
obs, action, reward, True
) # If the episode ends because max number of steps is reached, mark the transition as terminal
for c in self._controllers:
c.onActionTaken(self)
if is_terminal:
break
self._in_episode = False
for c in self._controllers:
c.onEpisodeEnd(self, is_terminal, reward)
return maxSteps
def _step(self):
"""
This method is called at each time step and performs one action in the environment.
Returns
-------
V : float
Estimated value function of current state.
action : int
The id of the action selected by the agent.
reward : float
Reward obtained for the transition
"""
action, V = self._chooseAction()
reward = 0
for i in range(self.sticky_action):
reward += self._environment.act(action)
return V, action, reward
def _addSample(self, ponctualObs, action, reward, is_terminal):
if self._mode != -1:
self._tmp_dataset.addSample(ponctualObs,
action,
reward,
is_terminal,
priority=1)
else:
self._dataset.addSample(ponctualObs,
action,
reward,
is_terminal,
priority=1)
def _chooseAction(self):
if self._mode != -1:
# Act according to the test policy if not in training mode
action, V = self._test_policy.action(self._state,
mode=self._mode,
dataset=self._dataset)
else:
if self._dataset.n_elems > self._replay_start_size:
# follow the train policy
action, V = self._train_policy.action(
self._state, mode=None, dataset=self._dataset
) #is self._state the only way to store/pass the state?
else:
# Still gathering initial data: choose dummy action
action, V = self._train_policy.randomAction()
for c in self._controllers:
c.onActionChosen(self, action)
return action, V
class NeuralAgent(object):
"""The NeuralAgent class wraps a deep Q-network for training and testing in a given environment.
Attach controllers to it in order to conduct an experiment (when to train the agent, when to test,...).
Parameters
-----------
environment : object from class Environment
The environment in which the agent interacts
q_network : object from class QNetwork
The q_network associated to the agent
replay_memory_size : int
Size of the replay memory. Default : 1000000
replay_start_size : int
Number of observations (=number of time steps taken) in the replay memory before starting learning. Default: minimum possible according to environment.inputDimensions().
batch_size : int
Number of tuples taken into account for each iteration of gradient descent. Default : 32
random_state : numpy random number generator
Default : random seed.
exp_priority : float
The exponent that determines how much prioritization is used, default is 0 (uniform priority).
One may check out Schaul et al. (2016) - Prioritized Experience Replay.
only_full_history : boolean
Whether we wish to train the neural network only on full histories or we wish to fill with zeroes the observations before the beginning of the episode
"""
def __init__(self,
environment,
q_network,
replay_memory_size=1000000,
replay_start_size=None,
batch_size=32,
random_state=np.random.RandomState(),
exp_priority=0,
train_policy=None,
test_policy=None,
only_full_history=True):
inputDims = environment.inputDimensions()
if replay_start_size == None:
replay_start_size = max(inputDims[i][0]
for i in range(len(inputDims)))
elif replay_start_size < max(inputDims[i][0]
for i in range(len(inputDims))):
raise AgentError(
"Replay_start_size should be greater than the biggest history of a state."
)
self._controllers = []
self._environment = environment
self._network = q_network
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size
self._batch_size = batch_size
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
self._dataset = DataSet(environment,
max_size=replay_memory_size,
random_state=random_state,
use_priority=self._exp_priority,
only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._mode_epochs_length = 0
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=config.floatX))
if (train_policy == None):
self._train_policy = EpsilonGreedyPolicy(q_network,
environment.nActions(),
random_state, 0.1)
else:
self._train_policy = train_policy
if (test_policy == None):
self._test_policy = EpsilonGreedyPolicy(q_network,
environment.nActions(),
random_state, 0.)
else:
self._test_policy = test_policy
def setControllersActive(self, toDisable, active):
""" Activate controller
"""
for i in toDisable:
self._controllers[i].setActive(active)
def setLearningRate(self, lr):
""" Set the learning rate for the gradient descent
"""
self._network.setLearningRate(lr)
def learningRate(self):
""" Get the learning rate
"""
return self._network.learningRate()
def setDiscountFactor(self, df):
""" Set the discount factor
"""
self._network.setDiscountFactor(df)
def discountFactor(self):
""" Get the discount factor
"""
return self._network.discountFactor()
def overrideNextAction(self, action):
""" Possibility to override the chosen action. This possibility should be used on the signal OnActionChosen.
"""
self._selected_action = action
def avgBellmanResidual(self):
""" Returns the average training loss on the epoch
"""
if (len(self._training_loss_averages) == 0):
return -1
return np.average(self._training_loss_averages)
def avgEpisodeVValue(self):
""" Returns the average V value on the episode
"""
if (len(self._Vs_on_last_episode) == 0):
return -1
return np.average(self._Vs_on_last_episode)
def totalRewardOverLastTest(self):
""" Returns the average sum of reward per episode
"""
return self._total_mode_reward / self._totalModeNbrEpisode
def bestAction(self):
""" Returns the best Action
"""
action = self._network.chooseBestAction(self._state)
V = max(self._network.qValues(self._state))
return action, V
def attach(self, controller):
if (isinstance(controller, controllers.Controller)):
self._controllers.append(controller)
else:
raise TypeError(
"The object you try to attach is not a Controller.")
def detach(self, controllerIdx):
return self._controllers.pop(controllerIdx)
def mode(self):
return self._mode
def startMode(self, mode, epochLength):
if self._in_episode:
raise AgentError(
"Trying to start mode while current episode is not yet finished. This method can be "
"called only *between* episodes for testing and validation.")
elif mode == -1:
raise AgentError(
"Mode -1 is reserved and means 'training mode'; use resumeTrainingMode() instead."
)
else:
self._mode = mode
self._mode_epochs_length = epochLength
self._total_mode_reward = 0.
del self._tmp_dataset
self._tmp_dataset = DataSet(
self._environment,
self._random_state,
max_size=self._replay_memory_size,
only_full_history=self._only_full_history)
def resumeTrainingMode(self):
self._mode = -1
def summarizeTestPerformance(self):
if self._mode == -1:
raise AgentError(
"Cannot summarize test performance outside test environment.")
self._environment.summarizePerformance(self._tmp_dataset)
def train(self):
# We make sure that the number of elements in the replay memory
# is strictly superior to self._replay_start_size before taking
# a random batch and perform training
if self._dataset.n_elems <= self._replay_start_size:
return
try:
states, actions, rewards, next_states, terminals, rndValidIndices = self._dataset.randomBatch(
self._batch_size, self._exp_priority)
loss, loss_ind = self._network.train(states, actions, rewards,
next_states, terminals)
self._training_loss_averages.append(loss)
if (self._exp_priority):
self._dataset.updatePriorities(
pow(loss_ind, self._exp_priority) + 0.0001,
rndValidIndices[1])
except SliceError as e:
warn("Training not done - " + str(e), AgentWarning)
def dumpNetwork(self, fname, nEpoch=-1):
""" Dump the network
Parameters
-----------
fname : string
Name of the file where the network will be dumped
nEpoch : int
Epoch number (Optional)
"""
try:
os.mkdir("nnets")
except Exception:
pass
basename = "nnets/" + fname
for f in os.listdir("nnets/"):
if fname in f:
os.remove("nnets/" + f)
all_params = self._network.getAllParams()
if (nEpoch >= 0):
joblib.dump(all_params, basename + ".epoch={}".format(nEpoch))
else:
joblib.dump(all_params, basename, compress=True)
def setNetwork(self, fname, nEpoch=-1):
""" Set values into the network
Parameters
-----------
fname : string
Name of the file where the values are
nEpoch : int
Epoch number (Optional)
"""
basename = "nnets/" + fname
if (nEpoch >= 0):
all_params = joblib.load(basename + ".epoch={}".format(nEpoch))
else:
all_params = joblib.load(basename)
self._network.setAllParams(all_params)
def run(self, n_epochs, epoch_length):
for c in self._controllers:
c.onStart(self)
i = 0
while i < n_epochs or self._mode_epochs_length > 0:
self._training_loss_averages = []
if self._mode != -1:
self._totalModeNbrEpisode = 0
while self._mode_epochs_length > 0:
self._totalModeNbrEpisode += 1
self._mode_epochs_length = self._runEpisode(
self._mode_epochs_length)
else:
length = epoch_length
while length > 0:
length = self._runEpisode(length)
i += 1
for c in self._controllers:
c.onEpochEnd(self)
self._environment.end()
for c in self._controllers:
c.onEnd(self)
def _runEpisode(self, maxSteps):
self._in_episode = True
initState = self._environment.reset(self._mode)
inputDims = self._environment.inputDimensions()
for i in range(len(inputDims)):
if inputDims[i][0] > 1:
self._state[i][1:] = initState[i][1:]
self._Vs_on_last_episode = []
while maxSteps > 0:
maxSteps -= 1
obs = self._environment.observe()
for i in range(len(obs)):
self._state[i][0:-1] = self._state[i][1:]
self._state[i][-1] = obs[i]
V, action, reward = self._step()
self._Vs_on_last_episode.append(V)
if self._mode != -1:
self._total_mode_reward += reward
is_terminal = self._environment.inTerminalState()
self._addSample(obs, action, reward, is_terminal)
for c in self._controllers:
c.onActionTaken(self)
if is_terminal:
break
self._in_episode = False
for c in self._controllers:
c.onEpisodeEnd(self, is_terminal, reward)
return maxSteps
def _step(self):
"""
This method is called at each time step. If the agent is currently in testing mode, and if its *test* replay
memory has enough samples, it will select the best action it can. If there are not enough samples, FIXME.
In the case the agent is not in testing mode, if its replay memory has enough samples, it will select the best
action it can with probability 1-CurrentEpsilon and a random action otherwise. If there are not enough samples,
it will always select a random action.
Parameters
-----------
state : ndarray
An ndarray(size=number_of_inputs, dtype='object), where states[input] is a 1+D matrix of dimensions
input.historySize x "shape of a given ponctual observation for this input".
Returns
-------
action : int
The id of the action selected by the agent.
V : float
Estimated value function of current state.
"""
action, V = self._chooseAction()
reward = self._environment.act(action)
return V, action, reward
def _addSample(self, ponctualObs, action, reward, is_terminal):
if self._mode != -1:
self._tmp_dataset.addSample(ponctualObs,
action,
reward,
is_terminal,
priority=1)
else:
self._dataset.addSample(ponctualObs,
action,
reward,
is_terminal,
priority=1)
def _chooseAction(self):
if self._mode != -1:
# Act according to the test policy if not in training mode
action, V = self._test_policy.act(self._state)
else:
if self._dataset.n_elems > self._replay_start_size:
# follow the train policy
action, V = self._train_policy.act(
self._state
) #is self._state the only way to store/pass the state?
else:
# Still gathering initial data: choose dummy action
action = self._random_state.randint(
0, self._environment.nActions())
V = 0
for c in self._controllers:
c.onActionChosen(self, action)
return action, V
def __init__(self,
inputDims,
q_network,
actions,
file='',
replay_memory_size=Parameters.REPLAY_MEMORY_SIZE,
replay_start_size=Parameters.BATCH_SIZE,
batch_size=Parameters.BATCH_SIZE,
random_state=np.random.RandomState(),
exp_priority=0,
batch_type='sequential',
train_policy=None,
test_policy=None,
only_full_history=True,
reward_as_input=False):
CompleteLearner.__init__(self, actions, file)
self.polfile = open(self.file + 'policy.txt', "w")
# if replay_start_size == None:
# replay_start_size = max(inputDims[i][0] for i in range(len(inputDims)))
# elif replay_start_size < max(inputDims[i][0] for i in range(len(inputDims))):
# raise AgentError("Replay_start_size should be greater than the biggest history of a state.")
self._controllers = []
# --- Bind controllers to the agent ---
# For comments, please refer to run_toy_env.py
self.attach(bc.VerboseController(evaluate_on='epoch', periodicity=1))
self.attach(
bc.TrainerController(evaluate_on='action',
periodicity=Parameters.UPDATE_FREQUENCY,
show_episode_avg_V_value=True,
show_avg_Bellman_residual=True))
self.attach(
bc.LearningRateController(
initial_learning_rate=Parameters.LEARNING_RATE,
learning_rate_decay=Parameters.LEARNING_RATE_DECAY,
periodicity=10000))
self.attach(
bc.DiscountFactorController(
initial_discount_factor=Parameters.DISCOUNT,
discount_factor_growth=Parameters.DISCOUNT_INC,
discount_factor_max=Parameters.DISCOUNT_MAX,
periodicity=10000))
self.attach(
bc.EpsilonController(initial_e=Parameters.EPSILON_START,
e_decays=Parameters.EPSILON_DECAY,
e_min=Parameters.EPSILON_MIN,
evaluate_on='action',
periodicity=1000,
reset_every='none'))
# self.attach(bc.InterleavedTestEpochController(
# id=0,
# epoch_length=Parameters.STEPS_PER_TEST,
# controllers_to_disable=[0, 1, 2, 3, 4],
# periodicity=2,
# show_score=True,
# summarize_every=-1))
self.obs = []
self.reward_as_input = reward_as_input
self._network = q_network
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size # make sure you gather this many observations before learning
self._batch_size = batch_size
self._batch_type = batch_type
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
#inputDimensions, n_actions, observation_type = np.float32, random_state = None, max_size = 1000, batch_type = 'random', only_full_history = True
self._dataset = DataSet(inputDimensions=inputDims,
n_actions=len(actions),
max_size=replay_memory_size,
random_state=random_state,
batch_type=self._batch_type,
only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._mode_epochs_length = 0
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
#self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=config.floatX))
if (train_policy == None):
self._train_policy = EpsilonGreedyPolicy(q_network, len(actions),
random_state, 0.05)
else:
self._train_policy = train_policy
if (test_policy == None):
self._test_policy = EpsilonGreedyPolicy(q_network, len(actions),
random_state, 0.)
else:
self._test_policy = test_policy
self.initEpisode()
class DeerLearner(CompleteLearner):
"""The NeuralAgent class wraps a deep Q-network for training and testing in a given environment.
Attach controllers to it in order to conduct an experiment (when to train the agent, when to test,...).
Parameters
-----------
environment : object from class Environment
The environment in which the agent interacts
q_network : object from class QNetwork
The q_network associated to the agent
replay_memory_size : int
Size of the replay memory. Default : 1000000
Size of the replay memory. Default : 1000000
replay_start_size : int
Number of observations (=number of time steps taken) in the replay memory before starting learning. Default: minimum possible according to environment.inputDimensions().
batch_size : int
Number of tuples taken into account for each iteration of gradient descent. Default : 32
random_state : numpy random number generator
Default : random seed.
exp_priority : float
The exponent that determines how much prioritization is used, default is 0 (uniform priority).
One may check out Schaul et al. (2016) - Prioritized Experience Replay.
train_policy : object from class Policy
Policy followed when in training mode (mode -1)
test_policy : object from class Policy
Policy followed when in other modes than training (validation and test modes)
only_full_history : boolean
Whether we wish to train the neural network only on full histories or we wish to fill with zeroes the observations before the beginning of the episode
"""
def __init__(self,
inputDims,
q_network,
actions,
file='',
replay_memory_size=Parameters.REPLAY_MEMORY_SIZE,
replay_start_size=Parameters.BATCH_SIZE,
batch_size=Parameters.BATCH_SIZE,
random_state=np.random.RandomState(),
exp_priority=0,
batch_type='sequential',
train_policy=None,
test_policy=None,
only_full_history=True,
reward_as_input=False):
CompleteLearner.__init__(self, actions, file)
self.polfile = open(self.file + 'policy.txt', "w")
# if replay_start_size == None:
# replay_start_size = max(inputDims[i][0] for i in range(len(inputDims)))
# elif replay_start_size < max(inputDims[i][0] for i in range(len(inputDims))):
# raise AgentError("Replay_start_size should be greater than the biggest history of a state.")
self._controllers = []
# --- Bind controllers to the agent ---
# For comments, please refer to run_toy_env.py
self.attach(bc.VerboseController(evaluate_on='epoch', periodicity=1))
self.attach(
bc.TrainerController(evaluate_on='action',
periodicity=Parameters.UPDATE_FREQUENCY,
show_episode_avg_V_value=True,
show_avg_Bellman_residual=True))
self.attach(
bc.LearningRateController(
initial_learning_rate=Parameters.LEARNING_RATE,
learning_rate_decay=Parameters.LEARNING_RATE_DECAY,
periodicity=10000))
self.attach(
bc.DiscountFactorController(
initial_discount_factor=Parameters.DISCOUNT,
discount_factor_growth=Parameters.DISCOUNT_INC,
discount_factor_max=Parameters.DISCOUNT_MAX,
periodicity=10000))
self.attach(
bc.EpsilonController(initial_e=Parameters.EPSILON_START,
e_decays=Parameters.EPSILON_DECAY,
e_min=Parameters.EPSILON_MIN,
evaluate_on='action',
periodicity=1000,
reset_every='none'))
# self.attach(bc.InterleavedTestEpochController(
# id=0,
# epoch_length=Parameters.STEPS_PER_TEST,
# controllers_to_disable=[0, 1, 2, 3, 4],
# periodicity=2,
# show_score=True,
# summarize_every=-1))
self.obs = []
self.reward_as_input = reward_as_input
self._network = q_network
self._replay_memory_size = replay_memory_size
self._replay_start_size = replay_start_size # make sure you gather this many observations before learning
self._batch_size = batch_size
self._batch_type = batch_type
self._random_state = random_state
self._exp_priority = exp_priority
self._only_full_history = only_full_history
#inputDimensions, n_actions, observation_type = np.float32, random_state = None, max_size = 1000, batch_type = 'random', only_full_history = True
self._dataset = DataSet(inputDimensions=inputDims,
n_actions=len(actions),
max_size=replay_memory_size,
random_state=random_state,
batch_type=self._batch_type,
only_full_history=self._only_full_history)
self._tmp_dataset = None # Will be created by startTesting() when necessary
self._mode = -1
self._mode_epochs_length = 0
self._total_mode_reward = 0
self._training_loss_averages = []
self._Vs_on_last_episode = []
#self._in_episode = False
self._selected_action = -1
self._state = []
for i in range(len(inputDims)):
self._state.append(np.zeros(inputDims[i], dtype=config.floatX))
if (train_policy == None):
self._train_policy = EpsilonGreedyPolicy(q_network, len(actions),
random_state, 0.05)
else:
self._train_policy = train_policy
if (test_policy == None):
self._test_policy = EpsilonGreedyPolicy(q_network, len(actions),
random_state, 0.)
else:
self._test_policy = test_policy
self.initEpisode()
def initEpisode(self):
for c in self._controllers:
c.onStart(self)
self._in_episode = True
# initState = environment.getInitState()
# inputDims = environment.inputDimensions()
# for i in range(len(inputDims)):
# if inputDims[i][0] > 1:
# self._state[i][1:] = initState[i][1:]
def setControllersActive(self, toDisable, active):
""" Activate controller
"""
for i in toDisable:
self._controllers[i].setActive(active)
def setLearningRate(self, lr):
""" Set the learning rate for the gradient descent
"""
self._network.setLearningRate(lr)
def learningRate(self):
""" Get the learning rate
"""
return self._network.learningRate()
def setDiscountFactor(self, df):
""" Set the discount factor
"""
self._network.setDiscountFactor(df)
def discountFactor(self):
""" Get the discount factor
"""
return self._network.discountFactor()
def overrideNextAction(self, action):
""" Possibility to override the chosen action. This possibility should be used on the signal OnActionChosen.
"""
self._selected_action = action
def avgBellmanResidual(self):
""" Returns the average training loss on the epoch
"""
if (len(self._training_loss_averages) == 0):
return -1
return np.average(self._training_loss_averages)
def avgEpisodeVValue(self):
""" Returns the average V value on the episode (on time steps where a non-random action has been taken)
"""
if (len(self._Vs_on_last_episode) == 0):
return -1
if (np.trim_zeros(self._Vs_on_last_episode) != []):
return np.average(np.trim_zeros(self._Vs_on_last_episode))
else:
return 0
def totalRewardOverLastTest(self):
""" Returns the average sum of rewards per episode and the number of episode
"""
return self._total_mode_reward / self._totalModeNbrEpisode, self._totalModeNbrEpisode
def bestAction(self):
""" Returns the best Action
"""
action = self._network.chooseBestAction(self._state)
V = max(self._network.qValues(self._state))
return action, V
def attach(self, controller):
if (isinstance(controller, controllers.Controller)):
self._controllers.append(controller)
else:
raise TypeError(
"The object you try to attach is not a Controller.")
def detach(self, controllerIdx):
return self._controllers.pop(controllerIdx)
def mode(self):
return self._mode
# def startMode(self, mode, epochLength):
# if self._in_episode:
# raise AgentError("Trying to start mode while current episode is not yet finished. This method can be "
# "called only *between* episodes for testing and validation.")
# elif mode == -1:
# raise AgentError("Mode -1 is reserved and means 'training mode'; use resumeTrainingMode() instead.")
# else:
# self._mode = mode
# self._mode_epochs_length = epochLength
# self._total_mode_reward = 0.
# del self._tmp_dataset
# self._tmp_dataset = DataSet(self._inputDimensions,len(self.actions),self._random_state, max_size=self._replay_memory_size,
# only_full_history=self._only_full_history, batch_type=self.batch_type)
def resumeTrainingMode(self):
self._mode = -1
def summarizeTestPerformance(self): # not needed
pass
def train(self):
# We make sure that the number of elements in the replay memory
# is strictly superior to self._replay_start_size before taking
# a random batch and perform training
if self._dataset.n_elems <= self._replay_start_size:
return
try:
# states, actions, rewards, next_states, terminals, rndValidIndices = self._dataset.randomBatch(
# self._batch_size, self._exp_priority)
loss, loss_ind = self._network.train(self.states, self.actions,
self.rewards,
self.next_states,
self.terminals)
self._training_loss_averages.append(loss)
# if (self._exp_priority):
# self._dataset.updatePriorities(pow(loss_ind, self._exp_priority) + 0.0001, rndValidIndices[1])
except SliceError as e:
warn("Training not done - " + str(e), AgentWarning)
def dumpNetwork(self, fname, nEpoch=-1):
""" Dump the network
Parameters
-----------
fname : string
Name of the file where the network will be dumped
nEpoch : int
Epoch number (Optional)
"""
try:
os.mkdir("nnets")
except Exception:
pass
basename = "nnets/" + fname
for f in os.listdir("nnets/"):
if fname in f:
os.remove("nnets/" + f)
all_params = self._network.getAllParams()
if (nEpoch >= 0):
joblib.dump(all_params, basename + ".epoch={}".format(nEpoch))
else:
joblib.dump(all_params, basename, compress=True)
def setNetwork(self, fname, nEpoch=-1):
""" Set values into the network
Parameters
-----------
fname : string
Name of the file where the values are
nEpoch : int
Epoch number (Optional)
"""
basename = "nnets/" + fname
if (nEpoch >= 0):
all_params = joblib.load(basename + ".epoch={}".format(nEpoch))
else:
all_params = joblib.load(basename)
self._network.setAllParams(all_params)
def run(self, n_epochs, epoch_length):
for c in self._controllers:
c.onStart(self)
i = 0
while i < n_epochs or self._mode_epochs_length > 0:
self._training_loss_averages = []
if self._mode != -1:
self._totalModeNbrEpisode = 0
while self._mode_epochs_length > 0:
self._totalModeNbrEpisode += 1
self._mode_epochs_length = self._runEpisode(
self._mode_epochs_length)
else:
length = epoch_length
while length > 0:
length = self._runEpisode(length)
i += 1
for c in self._controllers:
c.onEpochEnd(self)
# def end(self):
# self._environment.end()
# for c in self._controllers: c.onEnd(self)
@overrides
def setObservation(self, agent, environment):
environment.setObservation(agent)
@overrides
def performAction(self, agent, environment):
self.chosenAction.perform(agent, environment)
@overrides
def setAction(self):
pass
@overrides
def cycle(self, agent, environment):
environment.setObservation(agent)
if self.reward_as_input:
self.observation.append(self.r)
if len(self.obs) > self._dataset._batch_dimensions[0]:
self.obs.append(np.array(self.observation))
del self.obs[0]
for i in range(self._dataset._batch_dimensions[0]):
self._state[i][0:-1] = self._state[i][1:]
self._state[i][-1] = self.observation[i]
else:
self.obs.append(np.array(self.observation))
V, action = self._step()
self.chosenAction = self.actions[action]
self.chosenAction.perform([agent, environment])
self.setReward(environment.currentTask.reward_fun(agent, environment))
self._Vs_on_last_episode.append(V)
if self._mode != -1:
self._total_mode_reward += self.r
self._totalModeNbrEpisode += 1 #we will just define an episode as one time step
is_terminal = False #no terminal states in lifelong learning
self._addSample(self.observation, action, self.r, is_terminal)
self.environment = environment
for c in self._controllers:
c.onActionTaken(self)
for c in self._controllers:
c.onEpochEnd(self)
# if is_terminal:
# self.endEpisode(is_terminal,self.r) #though not needed for life-long learning ! ?
@overrides
def learn(self):
pass
@overrides
def printPolicy(self):
pass
# self.polfile.write("Final Pol: \n")
# self.polfile.write("\n")
# for s in len(self.states):
# qvals=self._network.qValues(self, s)
# for a in len(self.actions):
# self.polfile.write('('+str(s)+',' + self.actions[a].function.__name__+') : \t')
# self.polfile.write('Q=%.2f'%( qvals[a],))
@overrides
def reset(self):
pass
def endEpisode(self, is_terminal, reward):
self._in_episode = False
for c in self._controllers:
c.onEpisodeEnd(self, is_terminal, reward)
def _step(self):
"""
This method is called at each time step. If the agent is currently in testing mode, and if its *test* replay
memory has enough samples, it will select the best action it can. If there are not enough samples, FIXME.
In the case the agent is not in testing mode, if its replay memory has enough samples, it will select the best
action it can with probability 1-CurrentEpsilon and a random action otherwise. If there are not enough samples,
it will always select a random action.
Parameters
-----------
state : ndarray
An ndarray(size=number_of_inputs, dtype='object), where states[input] is a 1+D matrix of dimensions
input.historySize x "shape of a given ponctual observation for this input".
Returns
-------
action : int
The id of the action selected by the agent.
V : float
Estimated value function of current state.
"""
action, V = self._chooseAction()
# print('state='+str(self.observation))
# print('action='+str(self.actions[action].function.__name__))
# print('value='+str(V))
return V, action
def _addSample(self, ponctualObs, action, reward, is_terminal):
if self._mode != -1:
self._tmp_dataset.addSample(ponctualObs,
action,
reward,
is_terminal,
priority=1)
else:
self._dataset.addSample(ponctualObs,
action,
reward,
is_terminal,
priority=1)
def _chooseAction(self):
if self._mode != -1:
# Act according to the test policy if not in training mode
action, V = self._test_policy.action(self._state)
else:
if self._dataset.n_elems >= self._replay_start_size:
# follow the train policy
# follow the train policy
action, V = self._train_policy.action(
self._state
) # is self._state the only way to store/pass the state?
else:
# Still gathering initial data: choose dummy action
action, V = self._train_policy.randomAction()
for c in self._controllers:
c.onActionChosen(self, action)
return action, V
# --- Instantiate learning_algo ---
learning_algo = CRAR(
env,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_norm,
parameters.freeze_interval,
parameters.batch_size,
parameters.update_rule,
rng,
double_Q=True,
high_int_dim=HIGH_INT_DIM,
internal_dim=3)
train_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 1.) # always takes random actions
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 0.1) # random 1/10 times
# --- Instantiate agent ---
agent = NeuralAgent(
env,
learning_algo,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0] for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
train_policy=train_policy,
test_policy=test_policy)
# --- Create unique filename for FindBestController ---
h = hash(vars(parameters), hash_name="sha1")
