def agent_step(
reward, state
): # returns NumPy array, reward: floating point, this_observation: NumPy array
"""
Arguments: reward: floting point, state: integer
Returns: action: integer
"""
# select an action, based on Q
global last_x, last_y, last_action, Model, visited, Q
x, y = state
Q[last_x, last_y,
last_action] += alpha * (reward + gamma * np.max(Q[x, y, :]) -
Q[last_x, last_y, last_action])
Model[last_x, last_y, last_action, 0] = x
Model[last_x, last_y, last_action, 1] = y
Model[last_x, last_y, last_action, 2] = reward
visited.append((last_x, last_y, last_action))
Dyna_Q()
#action = np.argmax(Q[x, y, :])
action = np.random.choice(np.where(Q[x, y, :] == Q[x, y, :].max())[0])
if rand_un() < epsilon:
action = rand_in_range(4)
#print state, action
last_x = x
last_y = y
last_action = action
return last_action
def agent_step(reward, state):
global actions, old_state, old_action, iht, weights, z
#tile-coding
scaled_ns1 = 1. * NUM_TILES * (state[0] - POSITION[0]) / (POSITION[1] -
POSITION[0])
scaled_ns2 = 1. * NUM_TILES * (state[1] - VELOCITY[0]) / (VELOCITY[1] -
VELOCITY[0])
hash_s = old_state
hash_ns = np.asarray(tiles(iht, NUM_TILINGS, [scaled_ns1, scaled_ns2]))
#epsilon-greedy
rand = rand_un()
if rand < EPSILON:
n_action = random.choice(actions)
else:
n_action = np.argmax(np.sum(weights[:, hash_ns], axis=1))
#learning and update traces
q = np.sum(weights[old_action, hash_s])
nq = np.sum(weights[n_action, hash_ns])
z[old_action, hash_s] = 1.
weights += ALPHA * (reward + GAMMA * nq - q) * z
z *= GAMMA * LAMBDA
old_state = hash_ns
old_action = n_action
return n_action
def agent_step(
reward, this_observation
): # returns NumPy array, reward: floating point, observation_t: NumPy array
global local_action, last_observation, this_action, action_value_estimates, action_counts, time_step, C
#Update estimate for current action
cur_action = int(this_action[0])
action_value_estimates[cur_action] += alpha * (
reward - action_value_estimates[cur_action])
#Choose a new action using the parameter based agents
stp1 = this_observation[0]
action_selection_prob = rand_un()
if action_selection_prob <= (1 - epsilon):
atp1 = action_value_estimates.index(max(action_value_estimates))
elif episode == EPSILON_GREEDY:
atp1 = randInRange(numActions)
else:
print("BAD EPISODE: NO ACTION SELECTION FOR THE CURRENT AGENT!!!")
exit()
action_counts[atp1] += 1
time_step += 1
local_action[0] = atp1
this_action = local_action
last_observation = this_observation
return this_action
def agent_step(reward, state):
global cur_state, cur_action, weights, e_trace
next_state = state
#Update the weights
delta = reward
cur_state_feature_indices = approx_value(cur_state, cur_action, weights)[1]
for index in cur_state_feature_indices:
delta = delta - weights[0][index]
e_trace[0][index] = 1
#Choose the next action, epislon-greedy style
if rand_un() < 1 - EPSILON:
actions = [
approx_value(cur_state, action, weights)[0]
for action in range(NUM_ACTIONS)
]
next_action = actions.index(max(actions))
else:
next_action = rand_in_range(NUM_ACTIONS)
next_state_feature_indices = approx_value(next_state, next_action,
weights)[1]
for index in next_state_feature_indices:
delta = delta + GAMMA * weights[0][index]
weights += ALPHA * delta * e_trace
e_trace = GAMMA * LAMBDA * e_trace
cur_state = next_state
cur_action = next_action
return cur_action
def agent_step(
reward, state
): # returns NumPy array, reward: floating point, this_observation: NumPy array
"""
Arguments: reward: floting point, state: integer
Returns: action: integer
"""
global alpha, gamma, actions_permitted, Q, action_list, last_action, last_state
# select an action, based on Q
if rand_un() < epsilon:
action = action_list[rand_in_range(actions_permitted)]
else:
action = action_list[np.argmax(Q[int(state[0])][int(state[1])])]
Q[last_state[0], last_state[1],
find_action(last_action)] += alpha * (
reward + gamma *
Q[int(state[0]), int(state[1]),
find_action(action)] - Q[last_state[0], last_state[1],
find_action(last_action)])
last_action = action
last_state = state
return action
def agent_step(reward, state):
global q_values, actions, old_info, reward_buffer, gammas
x = state[0][0]
y = state[0][1]
hash_state = y * 10 + x
#epsilon greedy
rand = rand_un()
if rand <= EPSILON:
action = rand_in_range(len(actions))
else:
action = np.argmax(q_values[hash_state, :])
#learning
reward_buffer.append(reward)
if len(old_info) >= N:
old_state = old_info[0][0]
old_action = old_info[0][1]
q_values[old_state, old_action] += ALPHA * (np.sum(gammas * np.asarray(reward_buffer))+\
(GAMMA**(N)) * q_values[hash_state, action] - q_values[old_state, old_action])
old_info.pop(0)
reward_buffer.pop(0)
old_info.append((hash_state, action))
return actions[action]
def pick_action(arr, epsilon, num_actions):
arg_max = np.argmax(arr)
if rand_un() < epsilon:
action = rand_in_range(num_actions)
else:
action = arg_max
return action
def agent_step(
reward, state
): # returns NumPy array, reward: floating point, this_observation: NumPy array
global Q, actions, last_action, last_y, last_x
"""
Arguments: reward: floting point, state: integer
Returns: action: floating point
"""
# select an action, based on Q
# s'x and y
x = state[0]
y = state[1]
if rand_un() < epsilon:
action_index = rand_in_range(num_action)
else:
action_index = np.argmax(Q[x][y]) #find best action
#update last step's Q
Q[last_x][last_y][last_action] += alpha_step * (
reward + Q[x][y][action_index] - Q[last_x][last_y][last_action])
last_x = x
last_y = y
last_action = action_index
action = actions[action_index]
return action
def agent_step(reward, state): # returns NumPy array, reward: floating point, this_observation: NumPy array
"""
Arguments: reward: floting point, state: integer
Returns: action: integer
"""
# select an action, based on Q
global Q, last_action, S, S_, model, previous_states
S_ = state
if (S[0],S[1]) not in previous_states:
previous_states[(S[0],S[1])] = set()
previous_states[(S[0],S[1])].add(last_action)
Q[S[0]][S[1]][last_action] += alpha * (reward + gamma * max(Q[S_[0]][S_[1]]) - Q[S[0]][S[1]][last_action])
model[S[0]][S[1]][last_action] = (reward, S_[0], S_[1])
for i in range(n):
S_planning = random.choice(previous_states.keys())
A_planning = random.sample(previous_states[S_planning], 1)
reward_planning, x_planning, y_planning = model[S_planning[0]][S_planning[1]][A_planning[0]]
Q[S_planning[0]][S_planning[1]][A_planning[0]] += alpha * (reward_planning + gamma * max(Q[x_planning][y_planning]) - Q[S_planning[0]][S_planning[1]][A_planning[0]])
if rand_un() < epsilon:
action = rand_in_range(4)
else:
action = argmax(Q[S[0]][S[1]])
S = S_
last_action = action
return action
def agent_start(state): global Q,last_action,last_y,last_x,model """ Hint: Initialize the variavbles that you want to reset before starting a new episode Arguments: state: numpy array Returns: action: integer list """ # pick the first action, don't forget about exploring starts x = state[0] y = state[1] if rand_un() < epsilon: action_index = rand_in_range(num_action) else: action_index = np.argmax(Q[x][y]) #find best action if Q[x][y][action_index] == 0: action_index = rand_in_range(4) last_action = action_index last_x = x last_y = y action = actions[action_index] return action
def agent_step(reward, state):
global q_values, actions, trajectory
x = state[0][0]
y = state[0][1]
hash_state = y * 10 + x
#epsilon greedy
rand = rand_un()
if rand <= EPSILON:
action = rand_in_range(len(actions))
else:
action = np.argmax(q_values[hash_state, :])
#print state, action
#learning
n = len(trajectory)
for i, (s, a) in enumerate(trajectory):
#Wn = math.exp(-0.5*(g - (r+self.values[ns]))**2)
q_values[s, a] += 1. / N[s, a] * (GAMMA**(n - 1 - i)) * (
reward + GAMMA * q_values[hash_state, action] - q_values[s, a])
old_state = hash_state
old_action = action
#if (hash_state, action) not in trajectory:
trajectory.append((hash_state, action))
N[hash_state, action] += 1
return actions[action]
def env_step(action):
global current_state
reward = 0.0
is_terminal = False
#go left
if action == 'left':
left_stop = max(0, current_state[0] - 100)
left_range = current_state[0] - left_stop
left_prob = 1. * (100 - (left_range - 1)) / 100
rand = rand_un()
if rand <= left_prob:
current_state[0] = left_stop
else:
current_state[0] = random.choice(
range(left_stop + 1, current_state[0]))
#go right
else:
right_stop = min(1001, current_state[0] + 100)
right_range = right_stop - current_state[0]
right_prob = 1. * (100 - (right_range - 1)) / 100
rand = rand_un()
if rand <= right_prob:
current_state[0] = right_stop
else:
current_state[0] = random.choice(
range(current_state[0] + 1, right_stop))
if current_state[0] == 0:
is_terminal = True
current_state = None
reward = -1.0
elif current_state[0] == 1001:
is_terminal = True
current_state = None
reward = 1.0
result = {
"reward": reward,
"state": current_state,
"isTerminal": is_terminal
}
return result
def action_select(s):
if rand_un() < e:
#explore
return rand_in_range(4)
else:
#decide action based on policy
return np.random.choice(
np.where(Q[s[0]][s[1]] == np.amax(Q[s[0]][s[1]]))[0])
def agent_step(reward, state): # returns NumPy array, reward: floating point, this_observation: NumPy array
"""
Arguments: reward: should be a constant -1 until episode termination
Returns action as 0,1,or 2
"""
global old_state, iht, curr_state, last_action, alpha, w, gamma, num_tilings, tile_width, epsilon, z, lambd
action = None
curr_state = state
err = reward
#unpack the np arrays
x,xdot = state
o_x, o_xdot = old_state
#calculate indices of affected features of curr_state and old_state
features_old = tiles(iht,num_tilings,[((o_x/(0.5+1.2))*(1/tile_width)), ((o_xdot/(0.07+0.07))*(1/tile_width))],[last_action]) #x(s) components.
for i in features_old:
err -= w[i]
z[i] = 1.0
######## ACTION SELECTION ########
ExploreExploit = rand_un()
if ExploreExploit < epsilon:
action = random.randrange(0,3) # Explore
else:
## GREEDY ACTION ##
max_q = -999999999.9 #approx -inf
for i in range(0,3): #number of poss actions
Xs = tiles(iht,num_tilings,[((x/(0.5+1.2))*(1/tile_width)), ((xdot/(0.07+0.07))*(1/tile_width))], [i]) #x(s') components.
est_q = 0.0
for j in Xs:
est_q += w[j]
if est_q > max_q:
max_q = est_q
action = i
features_curr = tiles(iht,num_tilings,[((x/(0.5+1.2))*(1/tile_width)), ((xdot/(0.07+0.07))*(1/tile_width))], [action]) #x(s') components.
for i in features_curr:
err += gamma*w[i]
w += alpha*err*z
z = z*gamma*lambd
old_state = state
last_action = action
return action
def agent_step(reward, state):
global state_action_values, cur_state, cur_action, cur_epsilon
next_state = state
#Choose the next action, epsilon greedy style
if AGENT == TABULAR:
if rand_un() < 1 - cur_epsilon:
#Need to ensure that an action is picked uniformly at random from among those that tie for maximum
cur_max = state_action_values[state[0]][state[1]][0]
max_indices = [0]
for i in range(1, len(state_action_values[state[0]][state[1]])):
if state_action_values[state[0]][state[1]][i] > cur_max:
cur_max = state_action_values[state[0]][state[1]][i]
max_indices = [i]
elif state_action_values[state[0]][state[1]][i] == cur_max:
max_indices.append(i)
next_action = max_indices[rand_in_range(len(max_indices))]
else:
next_action = rand_in_range(NUM_ACTIONS)
#Update the state action values
next_state_max_action = state_action_values[next_state[0]][next_state[1]].index(max(state_action_values[next_state[0]][next_state[1]]))
state_action_values[cur_state[0]][cur_state[1]][cur_action] += ALPHA * (reward + GAMMA * state_action_values[next_state[0]][next_state[1]][next_state_max_action] - state_action_values[cur_state[0]][cur_state[1]][cur_action])
elif AGENT == NEURAL:
#Choose the next action, epsilon greedy style
if rand_un() < 1 - cur_epsilon:
#Get the best action over all actions possible in the next state,
q_vals = model.predict(encode_1_hot(next_state), batch_size=1)
q_max = np.max(q_vals)
next_action = np.argmax(q_vals)
cur_action_target = reward + GAMMA * q_max
#Get the value for the current state for which the action was just taken
cur_state_1_hot = encode_1_hot(cur_state)
q_vals = model.predict(cur_state_1_hot, batch_size=1)
q_vals[0][cur_action] = cur_action_target
model.fit(cur_state_1_hot, q_vals, batch_size=1, epochs=1, verbose=0)
else:
next_action = rand_in_range(NUM_ACTIONS)
cur_state = next_state
cur_action = next_action
return next_action
def agent_start(state):
global state_action_values, cur_state, cur_action
#Choose the next action, epsilon greedy style
cur_state = state
if rand_un() < 1 - EPSILON:
cur_action = state_action_values[state[0]][state[1]].index(max(state_action_values[state[0]][state[1]]))
else:
cur_action = rand_in_range(NUM_ACTIONS)
return cur_action
def pick_action(self):
if self._last_action == None:
self.pick_first_action()
# arg_max = self._find_max(self._Q)
arg_max = np.argmax(self._Q)
if rand_un() < self._epsilon:
action = rand_in_range(self._num_actions)
else:
action = arg_max
self._last_action = action
# print('action is {}'.format(self._last_action))
return action
def agent_step(reward, state): # returns NumPy array, reward: floating point, this_observation: NumPy array global Q,last_action,last_y,last_x,model """ Arguments: reward: floting point, state: integer Returns: action: floating point """ # select an action, based on Q # s'x and y x = state[0] y = state[1] Q[last_x][last_y][last_action] += alpha_step * (reward+ gamma*np.max(Q[x][y]) - Q[last_x][last_y][last_action]) modelKey = (last_x,last_y,last_action) model[modelKey] = [reward,x,y] i = 0 while i < n: i += 1 chosen = False while not chosen: modelX = rand_in_range(9) modelY = rand_in_range(6) modelA = rand_in_range(4) if model[(modelX,modelY,modelA)][0] != -1.0: chosen = True modelNextY = model[(modelX,modelY,modelA)][2] modelNextX = model[(modelX,modelY,modelA)][1] modelReward = model[(modelX,modelY,modelA)][0] Q[modelX][modelY][modelA] += alpha_step * (modelReward +gamma*np.max(Q[modelNextX][modelNextY]) - Q[modelX][modelY][modelA]) if rand_un() < epsilon: action_index = rand_in_range(num_action) else: action_index = np.argmax(Q[x][y]) #find best action if Q[x][y][action_index] == 0: action_index = rand_in_range(4) last_x = x last_y = y last_action = action_index action = actions[action_index] return action
def agent_step(
reward, state
): # returns NumPy array, reward: floating point, this_observation: NumPy array
global pre, policy, ph, e
if int(state) > int(pre[0]):
ph[0] += 1
ph[1] += 1
policy_create(pre, reward, state)
if rand_un() > e:
action = 1 + int(rand_un() * min(state, 99 - state + 1))
else:
action = int(policy[state])
pre[0] = state
pre[1] = action
"""
Arguments: reward: floting point, state: integer
Returns: action: integer
"""
# select an action, based on Q
return action
def agent_start(state):
global pre
"""
Hint: Initialize the variavbles that you want to reset before starting a new episode
Arguments: state: numpy array
Returns: action: integer
"""
# pick the first action, don't forget about exploring starts
action = 1 + int(rand_un() * min(state, 99 - state + 1))
pre[0] = state
pre[1] = action
return action
def agent_step(reward, state):
global state_action_values, cur_state, cur_action
next_state = state
if AGENT == TABULAR:
#Choose the next action, epsilon greedy style
cur_state = state
if rand_un() < 1 - EPSILON:
next_action = state_action_values[state[0]][state[1]].index(max(state_action_values[state[0]][state[1]]))
else:
next_action = rand_in_range(NUM_ACTIONS)
#Update the state action values
next_state_max_action = state_action_values[next_state[0]][next_state[1]].index(max(state_action_values[next_state[0]][next_state[1]]))
state_action_values[cur_state[0]][cur_state[1]][cur_action] += ALPHA * (reward + GAMMA * state_action_values[next_state[0]][next_state[1]][next_state_max_action] - state_action_values[cur_state[0]][cur_state[1]][cur_action])
#print(state_action_values)
elif AGENT == NEURAL:
#Choose the next action, epsilon greedy style
if rand_un() < 1 - EPSILON:
#Get the best action over all actions possible in the next state,
q_vals = model.predict(encode_1_hot(next_state), batch_size=1)
q_max = np.max(q_vals)
next_action = np.argmax(q_vals)
cur_action_target = reward + GAMMA * q_max
#Get the value for the current state for which the action was just taken
cur_state_1_hot = encode_1_hot(cur_state)
q_vals = model.predict(cur_state_1_hot, batch_size=1)
q_vals[0][cur_action] = cur_action_target
model.fit(cur_state_1_hot, q_vals, batch_size=1, epochs=1, verbose=0)
else:
next_action = rand_in_range(NUM_ACTIONS)
#print(AGENT)
#print(next_action)
cur_state = next_state
cur_action = next_action
return next_action
def agent_step(reward, state):
global old_action, old_state
if rand_un() < e:
action = rand_in_range(movement)
else:
action = np.argmax(Q[state[0]][state[1]])
Q[old_state[0]][old_state[1]][old_action] += a * (
reward + Q[state[0]][state[1]][action] -
Q[old_state[0]][old_state[1]][old_action])
old_state[0] = state[0]
old_state[1] = state[1]
old_action = action
return action
def agent_start(this_observation): # returns NumPy array, this_observation: NumPy array
global last_action
local_action = np.zeros(1)
if rand_un() < 0: # you may change it to 0 or 0.1
local_action[0] = rand_in_range(num_actions)
else:
local_action[0] = findGreedyAction()
last_action = local_action
return local_action[0]
def choose_action(state):
global Q
mx = np.amax(Q[state[0], state[1], :])
arg = np.argwhere(Q[state[0], state[1], :] == mx)
inds = np.reshape(arg, np.size(arg))
greedy_action = np.random.choice(inds)
toss = rand_un()
if toss > epsilon:
action = greedy_action
else:
action = rand_in_range(NUMBER_OF_ACTIONS)
return action
def agent_start(state):
global cur_state, cur_action, weights
cur_state = state
#Choose the next action, epislon-greedy style
if rand_un() < 1 - EPSILON:
actions = [
approx_value(cur_state, action, weights)[0]
for action in range(NUM_ACTIONS)
]
cur_action = actions.index(max(actions))
else:
cur_action = rand_in_range(NUM_ACTIONS)
return cur_action
def agent_step(reward, state):
global state_action_values, cur_state, cur_action
next_state = state
#Choose the next action, epsilon greedy style
if rand_un() < 1 - EPSILON:
next_action = state_action_values[state[0]][state[1]].index(max(state_action_values[state[0]][state[1]]))
else:
next_action = rand_in_range(NUM_ACTIONS)
#Update the state action value function
state_action_values[cur_state[0]][cur_state[1]][cur_action] += ALPHA * (reward + state_action_values[next_state[0]][next_state[1]][next_action] - state_action_values[cur_state[0]][cur_state[1]][cur_action])
cur_state = next_state
cur_action = next_action
return next_action
def agent_start(state):
global actions, old_state, old_action, q_values
x = state[0][0]
y = state[0][1]
hash_state = y * 10 + x # there are 70 states in total
rand = rand_un()
if rand <= EPSILON:
action = rand_in_range(len(actions))
else:
action = np.argmax(q_values[hash_state, :])
old_state = hash_state
old_action = action
return actions[action]
def agent_start(state):
"""
Hint: Initialize the variavbles that you want to reset before starting a new episode
Arguments: state: numpy array
Returns: action: integer
"""
global actions_permitted, Q, action_list, last_action, last_state
# pick the first action, don't forget about exploring starts
if rand_un() < epsilon:
action = action_list[rand_in_range(actions_permitted)]
else:
action = action_list[np.argmax(Q[state[0]][state[1]])]
last_action = action
last_state = state
return action
def agent_start(state):
global actions, old_info, reward_buffer
x = state[0][0]
y = state[0][1]
hash_state = y * 10 + x
rand = rand_un()
if rand <= EPSILON:
action = rand_in_range(len(actions))
else:
action = np.argmax(q_values[hash_state, :])
old_info = []
old_info.append((hash_state, action))
reward_buffer = []
return actions[action]
def env_step(
this_action
): # returns (floating point, NumPy array, Boolean), this_action: NumPy array
global local_observation, this_reward_observation #, nStatesSimpleEnv
episode_over = False
the_reward = randn(0.0, 1.0) # rewards drawn from (0, 1) Gaussian
atp1 = this_action[0] # how to extact action
stp1 = randInRange(
nStatesSimpleEnv) # state transitions are uniform random
if rand_un() < 0.05:
episode_over = True # termination is random
local_observation[0] = stp1
this_reward_observation = (the_reward, this_reward_observation[1],
episode_over)
return this_reward_observation