angle = 2*PI - abs(angle)
elif (angle > PI):
angle = -(2*PI - angle)
return angle
def inputDimensions(self):
return self._input_dim
def nActions(self):
# The environment allows two different actions to be taken
# at each time step
return self._n_actions
def observe(self):
return self.convert_repr()
if __name__ == "__main__":
rng = np.random.RandomState()
env = Doublecartpole(rng,min_f=-5,max_f=5,stepsize=5)
env.reset(mode=-1)
dataset = DataSet(env)
print(env.nActions())
for i in range(1000):
act = rng.randint(env.nActions())
r = env.act(act)
obs = env.observe()
dataset.addSample(obs, act, r, False, 0)
env.summarizePerformance(dataset,"./test/","testoide")
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.
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,
environments,
q_networks,
replay_memory_size=1000000,
replay_start_size=None,
batch_size=32,
random_state=np.random.RandomState(),
exp_priority=0,
train_policy=None,
train_policy_kwargs=None,
test_policy=None,
test_policy_kwargs=None,
only_full_history=True,
init_env=0):
self._controllers = []
self._environments = environments
self._networks = q_networks
self._e = init_env
self._environment = environments[self._e]
self._network = self._networks[self._e]
self._network_memory = []
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._datasets = list()
for i in range(len(self._environments)):
self._datasets.append(
DataSet(self._environments[i],
max_size=replay_memory_size,
random_state=random_state,
use_priority=self._exp_priority,
only_full_history=self._only_full_history))
self._dataset = self._datasets[self._e]
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._selected_batch = None
self._train_policies = [None] * len(environments)
self._test_policies = [None] * len(environments)
self._states = [None] * len(self._environments)
for i in range(len(self._environments)):
self._states[i] = []
inputDims = self._environments[i].inputDimensions()
if self._replay_start_size is None:
self._replay_start_size = max(inputDims[i][0]
for i in range(len(inputDims)))
elif self._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."
)
for j in range(len(inputDims)):
self._states[i].append(
np.zeros(inputDims[j], dtype=config.floatX))
if (train_policy is None):
self._train_policies[i] = EpsilonGreedyPolicy(
self._environments[i].nActions(), random_state, 0.1)
else:
#Todo : change the number of actions. Listify the policies
self._train_policies[i] = train_policy(
self._environments[i].nActions(), random_state,
**train_policy_kwargs)
self._train_policies[i].setAttribute("model", q_networks[i])
if (test_policy is None):
self._test_policies[i] = GreedyPolicy(
self._environments[i].nActions(), random_state)
else:
self._test_policies[i] = test_policy(
self._environments[i].nActions(), random_state,
**test_policy_kwargs)
self._test_policies[i].setAttribute("model", q_networks[i])
self._test_policy = self._test_policies[self._e]
self._train_policy = self._train_policies[self._e]
self._state = self._states[self._e]
def setEnvironment(self, e, reset=False):
""" Change the environment and the related dataset
"""
self._e = e
self._dataset = self._datasets[self._e]
self._state = self._states[e]
if reset:
self._state[...] = 0
self._dataset.flush()
if self._tmp_dataset is not None:
self._tmp_dataset.flush()
self._environment = self._environments[self._e]
self._train_policy = self._train_policies[self._e]
self._test_policy = self._test_policies[self._e]
self._network = self._networks[self._e]
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 statRewardsOverLastTests(self):
""" Returns the average sum of rewards per episode and the number of episode
"""
return np.mean(self._mode_rewards), np.var(self._mode_rewards), np.std(
self._mode_rewards), 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, 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, n_episodes=None):
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._n_episodes = self._n_episodes_init if n_episodes is None else n_episodes
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, n_episodes=None):
self._n_episodes = self._n_episodes_init if n_episodes is None else self._n_episodes
self._mode = -1
def summarizeTestPerformance(self, **kwargs):
if self._mode == -1:
raise AgentError(
"Cannot summarize test performance outside test environment.")
self._environment.summarizePerformance(self._tmp_dataset, **kwargs)
def generateAndStoreBatch(self):
self._selected_batch = self._dataset.randomBatch(
self._batch_size, self._exp_priority)
return copy.deepcopy(self._selected_batch)
def train(self):
"""
This function selects a random batch of data (with self._dataset.randomBatch) and performs a
Q-learning iteration (with self._network.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:
states, actions, rewards, next_states, terminals, rndValidIndices = self._dataset.randomBatch(
self._batch_size, self._exp_priority
) if self._selected_batch is None else self._selected_batch
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])
self._selected_batch = None
except SliceError as e:
warn("Training not done - " + str(e), AgentWarning)
def dumpNetwork(self, fname, nEpoch=-1, path="."):
""" Dump the network
Parameters
-----------
fname : string
Name of the file where the network will be dumped
nEpoch : int
Epoch number (Optional)
"""
try:
os.makedirs(path + "/nnets")
except Exception:
pass
basename = path + "/nnets/" + fname
for f in os.listdir(path + "/nnets/"):
if fname in f:
os.remove(path + "/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 storeNetwork(self, **kwargs):
""" Store a copy of the network in memory
"""
self._network_memory.append((self._network.getCopy(), kwargs))
def getNetworks(self):
""" Store a copy of the network in memory
"""
return self._network_memory
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, n_episodes=1):
"""
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 : number of epochs
int
epoch_length : maximum number of steps for a given epoch
int
"""
self._n_episodes = n_episodes
self._n_episodes_init = n_episodes
for c in self._controllers:
c.onStart(self)
i = 0
while i < n_epochs:
self._training_loss_averages = []
if self._mode != -1:
self._totalModeNbrEpisode = 0
self._mode_rewards = []
while self._totalModeNbrEpisode < self._n_episodes:
mode_epoch_length = self._mode_epochs_length
self._runEpisode(mode_epoch_length)
self._mode_rewards.append(self._total_mode_reward)
self._total_mode_reward = 0
self._totalModeNbrEpisode += 1
# mode_epoch_length = self._mode_epochs_length
# while mode_epoch_length > 0 and self._totalModeNbrEpisode < self._n_episodes:
# self._totalModeNbrEpisode += 1
# mode_epoch_length = self._runEpisode(mode_epoch_length)
#
# self._mode_rewards.append(self._total_mode_reward)
# self._total_mode_reward = 0
else:
length = epoch_length
n_episodes = self._n_episodes
while n_episodes > 0:
while length > 0:
length = self._runEpisode(length)
n_episodes -= 1
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 : maximum number of steps before automatically ending the episode
int
"""
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() or maxSteps == 0
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.action(self._state)
else:
if self._dataset.n_elems > self._replay_start_size:
# 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