def agent_step(reward, state):
next_state = state
#Choose the next action, epsilon greedy style
if rand_un() < 1 - a_globs.cur_epsilon or a_globs.is_trial_episode:
next_action = get_max_action_tabular(next_state)
else:
next_action = rand_in_range(a_globs.NUM_ACTIONS)
#Update the state action values
if not a_globs.is_trial_episode:
next_state_max_action = a_globs.state_action_values[next_state[0]][
next_state[1]].index(
max(a_globs.state_action_values[next_state[0]][next_state[1]]))
a_globs.state_action_values[a_globs.cur_state[0]][
a_globs.cur_state[1]][a_globs.cur_action] += a_globs.ALPHA * (
reward + a_globs.GAMMA * a_globs.state_action_values[
next_state[0]][next_state[1]][next_state_max_action] -
a_globs.state_action_values[a_globs.cur_state[0]][
a_globs.cur_state[1]][a_globs.cur_action])
a_globs.cur_state = next_state
a_globs.cur_action = next_action
return next_action
def agent_start(state):
a_globs.cur_state = state
if rand_un() < 1 - a_globs.cur_epsilon or a_globs.is_trial_episode:
a_globs.cur_action = get_max_action_tabular(a_globs.cur_state)
else:
a_globs.cur_action = rand_in_range(a_globs.NUM_ACTIONS)
return a_globs.cur_action
def agent_start(state):
#Context is a sliding window of the previous n states that gets added to the replay buffer used by auxiliary tasks
a_globs.cur_context = []
a_globs.cur_context_actions = []
a_globs.cur_state = state
if rand_un() < 1 - a_globs.cur_epsilon or a_globs.is_trial_episode:
a_globs.cur_action = get_max_action(a_globs.cur_state)
else:
a_globs.cur_action = rand_in_range(a_globs.NUM_ACTIONS)
return a_globs.cur_action
def sample_from_buffers(buffer_one, buffer_two=None):
"""
Pick from one of buffer one and buffer two according to the buffer probability parameter.
Then samples uniformly at random from the chosen buffer.
"""
#NOTE: Will have to add a check here to force sampling from non empty buffer if I decided to not wait for nonempty buffers of both types in reward/state tasks
if buffer_two is None or rand_un(
) <= a_globs.BUFFER_SAMPLE_BIAS_PROBABILITY:
cur_observation = buffer_one[rand_in_range(len(buffer_one))]
else:
cur_observation = buffer_two[rand_in_range(len(buffer_two))]
return cur_observation
def agent_start(state):
a_globs.cur_state = state
#Choose the next action, epislon-greedy style
if rand_un() < 1 - a_globs.cur_epsilon:
actions = [
approx_value(a_globs.cur_state, action, a_globs.weights)[0]
for action in range(a_globs.NUM_ACTIONS)
]
a_globs.cur_action = actions.index(max(actions))
else:
a_globs.cur_action = rand_in_range(a_globs.NUM_ACTIONS)
return a_globs.cur_action
def agent_step(reward, state):
next_state = state
#Update delta and the eligibility trace
delta = reward
_, a_globs.cur_state_feature_indices = approx_value(
a_globs.cur_state, a_globs.cur_action, a_globs.weights)
for index in a_globs.cur_state_feature_indices:
delta = delta - a_globs.weights[0][index]
a_globs.e_trace[0][index] = 1
#Choose the next action, epislon-greedy style
if rand_un() < 1 - a_globs.cur_epsilon:
actions = [
approx_value(a_globs.cur_state, action, a_globs.weights)[0]
for action in range(a_globs.NUM_ACTIONS)
]
next_action = actions.index(max(actions))
else:
next_action = rand_in_range(a_globs.NUM_ACTIONS)
#Update the a_globs.weights
_, next_state_feature_indices = approx_value(next_state, next_action,
a_globs.weights)
for index in next_state_feature_indices:
delta = delta + a_globs.GAMMA * a_globs.weights[0][index]
a_globs.weights += (a_globs.ALPHA /
a_globs.NUM_TILINGS) * delta * a_globs.e_trace
a_globs.e_trace = a_globs.GAMMA * a_globs.TRACE * a_globs.e_trace
a_globs.cur_state = next_state
a_globs.cur_action = next_action
# print(state)
# print(reward)
# print(next_action)
return a_globs.cur_action
def agent_step(reward, state):
next_state = state
update_replay_buffer(a_globs.cur_state, a_globs.cur_action, reward,
next_state)
next_state_formatted = format_states([next_state])
#Choose the next action, epsilon greedy style
if rand_un() < 1 - a_globs.cur_epsilon or a_globs.is_trial_episode:
#Get the best action over all actions possible in the next state, ie max_a(Q(s + 1), a))
q_vals = get_q_vals_aux(next_state, False)
next_action = np.argmax(q_vals)
else:
next_action = rand_in_range(a_globs.NUM_ACTIONS)
do_auxiliary_learning(a_globs.cur_state, next_state, reward)
if RL_num_steps() % a_globs.NUM_STEPS_TO_UPDATE == 0:
update_target_network()
a_globs.cur_state = next_state
a_globs.cur_action = next_action
return next_action
def do_auxiliary_learning(cur_state, next_state, reward):
"Update the weights for the auxiliary network based on both the current interaction with the environment and sampling from experience replay"
is_verbose = 0
#Perform direct learning on the current state and auxiliary information
q_vals = get_q_vals_aux(cur_state, False)
if next_state:
#Get the best action over all actions possible in the next state, ie max_a(Q(s + 1), a))
q_vals_next = get_q_vals_aux(next_state, True)
cur_action_target = reward + (a_globs.GAMMA * np.max(q_vals_next))
q_vals[0][a_globs.cur_action] = cur_action_target
else:
q_vals[0][a_globs.cur_action] = reward
if a_globs.AGENT == a_globs.REWARD:
#We make the rewards positive since we care only about the binary
#distinction between zero and non zero rewards and theano binary
#cross entropy loss requires targets to be 0 or 1
aux_target = np.array([[reward]])
elif a_globs.AGENT == a_globs.STATE:
if next_state:
aux_target = format_states([next_state])
else:
aux_target = np.zeros(shape=(
1,
a_globs.FEATURE_VECTOR_SIZE,
))
elif a_globs.AGENT == a_globs.NOISE:
aux_target = np.array([
rand_un() for i in range(a_globs.NUM_NOISE_NODES)
]).reshape(1, a_globs.NUM_NOISE_NODES)
elif a_globs.AGENT == a_globs.REDUNDANT:
nested_target = [q_vals for i in range(a_globs.NUM_REDUNDANT_TASKS)]
aux_target = np.array([
item for sublist in nested_target for item in sublist
]).reshape(1, a_globs.NUM_ACTIONS * a_globs.NUM_REDUNDANT_TASKS)
cur_state_formatted = format_states([cur_state])
#Check and see if the relevant buffer is non-empty
if buffers_are_ready(a_globs.buffer_container,
a_globs.BUFFER_SIZE) and not a_globs.is_trial_episode:
#Create the target training batch
batch_inputs = np.empty(shape=(
a_globs.BATCH_SIZE,
a_globs.FEATURE_VECTOR_SIZE,
))
batch_targets = np.empty(shape=(a_globs.BATCH_SIZE,
a_globs.NUM_ACTIONS))
batch_aux_targets = np.empty(shape=(a_globs.BATCH_SIZE,
aux_target.shape[1]))
#Add the current observation to the mini-batch
batch_inputs[0] = cur_state_formatted
batch_targets[0] = q_vals
batch_aux_targets[0] = aux_target[0]
#Use the replay buffer to learn from previously visited states
for i in range(1, a_globs.BATCH_SIZE):
cur_observation = do_buffer_sampling()
#NOTE: For now If N > 1 we only want the most recent state associated
#with the reward and next state (effectively setting N > 1 changes nothing right now since we want to use the same input type as in the regular singel task case)
most_recent_obs_state = cur_observation.states[-1]
sampled_state_formatted = format_states([most_recent_obs_state])
sampled_next_state_formatted = format_states(
[cur_observation.next_state])
#Get the best action over all actions possible in the next state, ie max_a(Q(s + 1), a))
q_vals = get_q_vals_aux(cur_observation.next_state, True)
cur_action_target = reward + (a_globs.GAMMA * np.max(q_vals))
#Get the value for the current state of the action which was just taken, ie Q(S, A),
#and set the target for the specifc action taken (we need to pass in the
#whole vector of q_values, since our network takes state only as input)
q_vals = get_q_vals_aux(most_recent_obs_state, False)
q_vals[0][a_globs.cur_action] = cur_action_target
if a_globs.AGENT == a_globs.REWARD:
#We make the rewards positive since we care only about the binary
#distinction between zero and non zero rewards and theano binary
#cross entropy loss requires targets to be 0 or 1
aux_target = np.array([[cur_observation.reward]])
elif a_globs.AGENT == a_globs.STATE:
aux_target = format_states([cur_observation.next_state])
elif a_globs.AGENT == a_globs.NOISE:
aux_target = np.array([
rand_un() for _ in range(a_globs.NUM_NOISE_NODES)
]).reshape(1, a_globs.NUM_NOISE_NODES)
elif a_globs.AGENT == a_globs.REDUNDANT:
nested_target = [
q_vals for _ in range(a_globs.NUM_REDUNDANT_TASKS)
]
aux_target = np.array([
item for sublist in nested_target for item in sublist
]).reshape(1,
a_globs.NUM_ACTIONS * a_globs.NUM_REDUNDANT_TASKS)
#print(i)
batch_inputs[i] = sampled_state_formatted
batch_targets[i] = q_vals
batch_aux_targets[i] = aux_target[0]
#Update the weights using the sampled batch
a_globs.model.fit(batch_inputs, [batch_targets, batch_aux_targets],
batch_size=a_globs.BATCH_SIZE,
epochs=1,
verbose=0)
def agent_step(reward, state):
next_state = state
next_state_formatted = format_states([next_state])
if not a_globs.is_trial_episode:
update_replay_buffer(a_globs.cur_state, a_globs.cur_action, reward,
next_state)
#Choose the next action, epsilon greedy style
if rand_un() < 1 - a_globs.cur_epsilon or a_globs.is_trial_episode:
#Get the best action over all actions possible in the next state, max_a(Q(s + 1), a))
q_vals = a_globs.model.predict(next_state_formatted, batch_size=1)
next_action = np.argmax(q_vals)
else:
next_action = rand_in_range(a_globs.NUM_ACTIONS)
#Get the target value for the update from the target network
q_vals = a_globs.target_network.predict(next_state_formatted, batch_size=1)
cur_action_target = reward + a_globs.GAMMA * np.max(q_vals)
#Get the value for the current state of the action which was just taken, ie Q(S, A),
#and set the target for the specifc action taken (we need to pass in the
#whole vector of q_values, since our network takes state only as input)
cur_state_formatted = format_states([a_globs.cur_state])
q_vals = a_globs.model.predict(cur_state_formatted, batch_size=1)
q_vals[0][a_globs.cur_action] = cur_action_target
#Check and see if the relevant buffer is non-empty
if buffers_are_ready(a_globs.buffer_container,
a_globs.BUFFER_SIZE) and not a_globs.is_trial_episode:
if (a_globs.is_trial_episode):
exit("BAD!")
buffer_states = [
observation.states for observation in a_globs.buffer_container[0]
]
#print(buffer_states)
#Create the target training batch
batch_inputs = np.empty(shape=(
a_globs.BATCH_SIZE,
a_globs.FEATURE_VECTOR_SIZE,
))
batch_targets = np.empty(shape=(a_globs.BATCH_SIZE,
a_globs.NUM_ACTIONS))
#Add the current observation to the mini-batch
batch_inputs[0] = cur_state_formatted
batch_targets[0] = q_vals
#Use the replay buffer to learn from previously visited states
for i in range(1, a_globs.BATCH_SIZE):
cur_observation = do_buffer_sampling()
#NOTE: For now If N > 1 we only want the most recent state associated with the reward and next state (effectively setting N > 1 changes nothing right now since we want to use the same input type as in the regular singel task case)
most_recent_obs_state = cur_observation.states[-1]
sampled_state_formatted = format_states([most_recent_obs_state])
sampled_next_state_formatted = format_states(
[cur_observation.next_state])
#Get the best action over all actions possible in the next state, ie max_a(Q(s + 1), a))
q_vals = a_globs.target_network.predict(
sampled_next_state_formatted, batch_size=1)
cur_action_target = reward + (a_globs.GAMMA * np.max(q_vals))
#Get the q_vals to adjust the learning target for the current action taken
q_vals = a_globs.model.predict(sampled_state_formatted,
batch_size=1)
q_vals[0][a_globs.cur_action] = cur_action_target
batch_inputs[i] = sampled_state_formatted
batch_targets[i] = q_vals
#Update the weights using the sampled batch
if not a_globs.is_trial_episode:
a_globs.model.fit(batch_inputs,
batch_targets,
batch_size=a_globs.BATCH_SIZE,
epochs=1,
verbose=0)
if RL_num_steps(
) % a_globs.NUM_STEPS_TO_UPDATE == 0 and not a_globs.is_trial_episode:
update_target_network()
a_globs.cur_state = next_state
a_globs.cur_action = next_action
return next_action
