def _q_learning_training(self, sensation, action, reward, next_sensation):
"""
Do a single Q-lambda training step given (s,a,r,s'). Can be
called from outside the q_learning_step method for off-policy
training, experience replay, etc.
"""
rho = self.rho(reward)
last_Q = self.Q(sensation)
last_value = last_Q[action]
if is_terminal(next_sensation):
value = 0
else:
value = max(self.Q(next_sensation))
delta = rho + (self.gamma * value - last_value)
self.verbose(
"r = %.5f, Q(t-1) = %.5f, Q(t) = %.5f, diff = %.5f, delta = %.5f, terminal? = %d"
% (rho, last_value, value, value - last_value, delta,
is_terminal(next_sensation)))
self.update_Q(sensation,
action,
delta,
on_policy=(last_Q[action] == max(last_Q)))
if delta:
assert (self.Q(sensation, action) - last_value) / delta < 1.0
def _q_learning_training(self,sensation,action,reward,next_sensation):
"""
Do a single Q-lambda training step given (s,a,r,s'). Can be
called from outside the q_learning_step method for off-policy
training, experience replay, etc.
"""
rho = self.rho(reward)
last_Q = self.Q(sensation)
last_value = last_Q[action]
if is_terminal(next_sensation):
value = 0
else:
value = max(self.Q(next_sensation))
delta = rho + (self.gamma * value - last_value)
self.verbose("r = %.5f, Q(t-1) = %.5f, Q(t) = %.5f, diff = %.5f, delta = %.5f, terminal? = %d"
% (rho,last_value,value,value-last_value,delta,is_terminal(next_sensation)))
self.update_Q(sensation,action,delta,on_policy = (last_Q[action] == max(last_Q)))
if delta:
assert (self.Q(sensation,action) - last_value)/delta < 1.0
def __call__(self, sensation, reward=None):
if not is_terminal(sensation):
sensation = tile_uniform_rfs(
array(sensation) / self.tile_width, self.num_rfs,
self.rf_width, self.num_tilings,
self.num_features / self.num_rfs)
return super(UniformTiledAgent, self).__call__(sensation, reward)
def __call__(self,sensation,reward=None):
if reward == None:
self.setup_gen()
if not rl.is_terminal(sensation):
try:
return self.gen.next()
except StopIteration:
return OPTION_TERMINATED
def __call__(self, sensation, reward=None):
if reward == None:
self.setup_gen()
if not rl.is_terminal(sensation):
try:
return self.gen.next()
except StopIteration:
return OPTION_TERMINATED
def __call__(self,sensation,reward=None):
if not is_terminal(sensation):
assert(type(sensation) == int)
s = zeros(self.num_features)
s[sensation] = 1.0
else:
s = sensation
return super(LinearTabularTDAgent,self).__call__(s,reward)
def __call__(self, sensation, reward=None):
if is_terminal(sensation):
new_sensation = sensation
else:
new_sensation = zeros(self.num_features, 'f')
for f in sensation:
new_sensation[f] = 1
return super(LinearListAgent, self).__call__(new_sensation, reward)
def __call__(self,sensation,reward=None):
if not is_terminal(sensation):
sensation = tile_uniform_rfs(array(sensation)/self.tile_width,
self.num_rfs,
self.rf_width,
self.num_tilings,
self.num_features/self.num_rfs)
return super(UniformTiledAgent,self).__call__(sensation,reward)
def __call__(self, sensation, reward=None):
if not is_terminal(sensation):
assert (type(sensation) == int)
s = zeros(self.num_features)
s[sensation] = 1.0
else:
s = sensation
return super(LinearTabularTDAgent, self).__call__(s, reward)
def __call__(self,sensation,reward=None):
if is_terminal(sensation):
new_sensation = sensation
else:
new_sensation = zeros(self.num_features,'f')
for f in sensation:
new_sensation[f] = 1
return super(LinearListAgent,self).__call__(new_sensation,reward)
def sarsa_step(self,sensation,reward=None):
"""
Do a step using the SARSA update method. Selects an action,
computes the TD update and calls self.update_Q. Returns the
agent's next action.
"""
if reward == None:
return self._start_episode(sensation)
rho = self.rho(reward)
next_action = self.policy(sensation)
if is_terminal(sensation):
value = 0
else:
value = self.Q(sensation,next_action)
last_value = self.Q(self.last_sensation,self.last_action)
delta = rho + (self.gamma * value - last_value)
self.verbose("controller step = %d, rho = %.2f"
% (self.total_steps,rho))
self.verbose(("Q(t-1) = %.5f, Q(t) = %.5f, diff = %.5f,"+
"delta = %.5f, terminal? = %d")
% (last_value,value,value-last_value,
delta,is_terminal(sensation)))
if self.allow_learning:
self.update_Q(self.last_sensation,self.last_action,delta)
self.last_sensation = sensation
self.last_action = next_action
if isinstance(reward,list):
self.total_steps += len(reward)
else:
self.total_steps += 1
return next_action
def sarsa_step(self, sensation, reward=None):
"""
Do a step using the SARSA update method. Selects an action,
computes the TD update and calls self.update_Q. Returns the
agent's next action.
"""
if reward == None:
return self._start_episode(sensation)
rho = self.rho(reward)
next_action = self.policy(sensation)
if is_terminal(sensation):
value = 0
else:
value = self.Q(sensation, next_action)
last_value = self.Q(self.last_sensation, self.last_action)
delta = rho + (self.gamma * value - last_value)
self.verbose("controller step = %d, rho = %.2f" %
(self.total_steps, rho))
self.verbose(("Q(t-1) = %.5f, Q(t) = %.5f, diff = %.5f," +
"delta = %.5f, terminal? = %d") %
(last_value, value, value - last_value, delta,
is_terminal(sensation)))
if self.allow_learning:
self.update_Q(self.last_sensation, self.last_action, delta)
self.last_sensation = sensation
self.last_action = next_action
if isinstance(reward, list):
self.total_steps += len(reward)
else:
self.total_steps += 1
return next_action
def policy(self,sensation):
"""
Given a sensation, return an action. Uses
self.action_selection to get a distribution over the agent's
actions. Uses self.applicable_actions to prevent selecting
inapplicable actions.
Returns 0 if is_terminal(sensation).
"""
if not is_terminal(sensation):
actions = self.applicable_actions(sensation)
return actions[weighted_sample(self.policy_fn(sensation,actions))]
else:
# In the terminal state, the action is irrelevant
return 0
def policy(self, sensation):
"""
Given a sensation, return an action. Uses
self.action_selection to get a distribution over the agent's
actions. Uses self.applicable_actions to prevent selecting
inapplicable actions.
Returns 0 if is_terminal(sensation).
"""
if not is_terminal(sensation):
actions = self.applicable_actions(sensation)
return actions[weighted_sample(self.policy_fn(sensation, actions))]
else:
# In the terminal state, the action is irrelevant
return 0
def __call__(self, sensation, reward=None):
"""
Do a step. Calls the function selected in self.step_method
and returns the action.
"""
step_fn = getattr(self, self.step_method + '_step')
action_index = step_fn(sensation, reward)
if self.history_log:
if reward is None:
self._history_file.write('start\n')
self._history_file.write( ` sensation ` + '\n')
self._history_file.write( ` reward ` + '\n')
if not is_terminal(sensation):
self._history_file.write( ` action_index ` + '\n')
return self.actions[action_index]
def __call__(self,sensation,reward=None):
"""
Do a step. Calls the function selected in self.step_method
and returns the action.
"""
step_fn = getattr(self,self.step_method+'_step')
action_index = step_fn(sensation,reward)
if self.history_log:
if reward is None:
self._history_file.write('start\n')
self._history_file.write(`sensation`+'\n')
self._history_file.write(`reward`+'\n')
if not is_terminal(sensation):
self._history_file.write(`action_index`+'\n')
return self.actions[action_index]
def __call__(self,sensation,reward=None):
if reward is None:
self._memory = [sensation]
else:
self._memory.append(sensation)
if is_terminal(sensation):
return super(TabularMemoryTDAgent,self).__call__(sensation,reward)
else:
action = super(TabularMemoryTDAgent,self).__call__(tuple(self._memory),reward)
assert self.actions[self.last_action] == action
self._memory.append(self.last_action)
if len(self._memory) > (2*self.memory_steps + 1):
del self._memory[0:2]
return action
def __call__(self, sensation, reward=None):
if reward is None:
self._memory = [sensation]
else:
self._memory.append(sensation)
if is_terminal(sensation):
return super(TabularMemoryTDAgent,
self).__call__(sensation, reward)
else:
action = super(TabularMemoryTDAgent,
self).__call__(tuple(self._memory), reward)
assert self.actions[self.last_action] == action
self._memory.append(self.last_action)
if len(self._memory) > (2 * self.memory_steps + 1):
del self._memory[0:2]
return action
def __call__(self,sensation,reward=None):
if reward == None:
self.stack = Stack([])
self.push_option(self.root_option)
self.last_sensation = sensation
self.last_reward = reward
if rl.is_terminal(sensation):
# unwind the stack giving everyone the current reward
# TODO: when options get their own separate rewards, this may change
while not self.stack.empty():
option,reward_list = self.stack.pop()
option(sensation,reward_list+[option.reward(sensation,reward)])
return None
else:
for option,rewards in self.stack[:-1]:
rewards.append(option.reward(sensation,reward))
option,rewards = self.stack.top()
return self.haction(option(sensation,option.reward(sensation,reward)))
def __call__(self, sensation, reward=None):
if reward == None:
self.stack = Stack([])
self.push_option(self.root_option)
self.last_sensation = sensation
self.last_reward = reward
if rl.is_terminal(sensation):
# unwind the stack giving everyone the current reward
# TODO: when options get their own separate rewards, this may change
while not self.stack.empty():
option, reward_list = self.stack.pop()
option(sensation, reward_list + [option.reward(sensation, reward)])
return None
else:
for option, rewards in self.stack[:-1]:
rewards.append(option.reward(sensation, reward))
option, rewards = self.stack.top()
return self.haction(option(sensation, option.reward(sensation, reward)))
def observe(self):
if self.actionQueue.empty() and not self.observe_wait:
print 'perform:obs', 'Observe', time.localtime()
self.observe_wait = 'Active Observe'
self.actionQueue.put(('Observe', time.localtime()))
observed,reward = self.observationQueue.get()
# Catch State change report
if reward == None: # End of episode
self.observed = self.reward = None
print 'Queue.observe() => observed', observed, 'reward', reward,self.observe_wait
if self.observe_wait not in ('Active Observe', ):
self.observe_wait = False
if rl.is_terminal(observed):
print 'Queue.observe() observed is terminal:', observed,self.observe_wait
self.observed,self.reward = [],reward
observed = None
if self.observe_wait not in ('Active Observe','State'):
self.observe_wait = 'Terminal'
if not self.actionQueue.full():
time.sleep(0.1)
print 'perform:obs', 'Observe', time.localtime()
self.actionQueue.put(('Observe', time.localtime()))
if type(observed) == str:
observed = self.str2meaning(observed)
if self.is_state(observed):
self.pomdp_state = observed
print 'Queue.observe() observed is a state:', observed,self.observe_wait
observed = None
if self.observe_wait in ('State','Terminal'):
self.observe_wait = False
while not observed:
observed = self.observe()
self.observe_wait = False
self.observed,self.reward = observed,reward
if not self.reward: self.reward = 0
return self.observed
def observe(self):
if self.actionQueue.empty() and not self.observe_wait:
print 'perform:obs', 'Observe', time.localtime()
self.observe_wait = 'Active Observe'
self.actionQueue.put(('Observe', time.localtime()))
observed, reward = self.observationQueue.get()
# Catch State change report
if reward == None: # End of episode
self.observed = self.reward = None
print 'Queue.observe() => observed', observed, 'reward', reward, self.observe_wait
if self.observe_wait not in ('Active Observe', ):
self.observe_wait = False
if rl.is_terminal(observed):
print 'Queue.observe() observed is terminal:', observed, self.observe_wait
self.observed, self.reward = [], reward
observed = None
if self.observe_wait not in ('Active Observe', 'State'):
self.observe_wait = 'Terminal'
if not self.actionQueue.full():
time.sleep(0.1)
print 'perform:obs', 'Observe', time.localtime()
self.actionQueue.put(('Observe', time.localtime()))
if type(observed) == str:
observed = self.str2meaning(observed)
if self.is_state(observed):
self.pomdp_state = observed
print 'Queue.observe() observed is a state:', observed, self.observe_wait
observed = None
if self.observe_wait in ('State', 'Terminal'):
self.observe_wait = False
while not observed:
observed = self.observe()
self.observe_wait = False
self.observed, self.reward = observed, reward
if not self.reward: self.reward = 0
return self.observed
def __call__(self, sensation, reward=None):
result = self.agent(sensation, reward)
if rl.is_terminal(sensation):
result = OPTION_TERMINATED
return result
def __call__(self,sensation,reward=None):
result = self.agent(sensation,reward)
if rl.is_terminal(sensation):
result = OPTION_TERMINATED
return result
