def sarsa(env, learn_st, test_st, learning=0.5, discount=0.99, back=False):
# Initialize Q table
Q = np.random.uniform(low=-1, high=1, size=(11, 11, env.action_space.n))
learning_b = 0.05
discount_b = 0.99
# Initialize variables to track results
learn_len = 0
learn_cumr = 0
tot_episodes = 0
# Decrease temp every turn
temp = TEMP
decay = (TEMP - TEMP_MIN) / 5000
reached = 0
# Run Q learning algorithm
while reached < 300:
# Initialize parameters
done = False
tot_reward, reward = 0, 0
x = np.random.choice(300)
pos, vel = learn_st[x]
state = env.reset(pos, vel)
M = list()
steps = 0
# Discretize state
state_adj = discretize(state)
state_tup = (state_adj[0], state_adj[1])
# Determine action - Boltzmann policy
action = boltz_policy(state_adj[0], state_adj[1], Q, temp)
while done != True:
# Get next state and reward
state2, reward, done, info = env.step(action)
steps += 1
# Discretize state2
state2_adj = discretize(state2)
state2_tup = (state2_adj[0], state2_adj[1])
# Determine action in state2 - Boltzmann policy
action2 = boltz_policy(state2_adj[0], state2_adj[1], Q, temp)
# Store s, a, r, s' in M
if back:
M.append((state_tup, action, reward, state2_tup))
if done and reward == 200:
reached += 1
# Adjust Q value for current state
else:
delta = learning * (reward + discount *
Q[state2_adj[0], state2_adj[1], action2] -
Q[state_adj[0], state_adj[1], action])
Q[state_adj[0], state_adj[1], action] += delta
state_adj = state2_adj
action = action2
# Update variables
tot_reward += reward
state_adj = state2_adj
while back and tot_reward > -2500 and steps > 0:
state1, action, reward, state2 = M.pop()
state11, state12 = state1
state21, state22 = state2
Q[state11, state12,
action] += learning_b * (reward +
discount_b * max(Q[state21, state22]) -
Q[state11, state12, action])
steps -= 1
if back:
M.clear()
if temp > TEMP_MIN:
temp -= decay
tot_episodes += 1
# Track mean length of episodes and mean cumulative reward
learn_len = (learn_len * (tot_episodes - 1) + steps) / tot_episodes
learn_cumr = (learn_cumr *
(tot_episodes - 1) + tot_reward) / tot_episodes
test_len = 0
test_cumr = 0
tot_episodes = 0
reached = 0
# Run SARSA algorithm: Testing phase
while reached < 40:
# Initialize parameters
done = False
tot_reward, reward = 0, 0
pos, vel = test_st[reached]
state = env.reset(pos, vel)
M = list()
steps = 0
# Discretize state
state_adj = discretize(state)
state_tup = (state_adj[0], state_adj[1])
# Determine action - Boltzmann policy
action = boltz_policy(state_adj[0], state_adj[1], Q, temp)
while done != True:
# Get next state and reward
state2, reward, done, info = env.step(action)
steps += 1
# Discretize state2
state2_adj = discretize(state2)
state2_tup = (state2_adj[0], state2_adj[1])
# Determine action in state2 - Boltzmann policy
action2 = boltz_policy(state2_adj[0], state2_adj[1], Q, temp)
# Store s, a, r, s' in M
if back:
M.append((state_tup, action, reward, state2_tup))
if done:
reached += 1
# Adjust Q value for current state
else:
delta = learning * (reward + discount *
Q[state2_adj[0], state2_adj[1], action2] -
Q[state_adj[0], state_adj[1], action])
Q[state_adj[0], state_adj[1], action] += delta
state_adj = state2_adj
action = action2
# Update variables
tot_reward += reward
state_adj = state2_adj
while back and tot_reward > -2500 and steps > 0:
state1, action, reward, state2 = M.pop()
state11, state12 = state1
state21, state22 = state2
Q[state11, state12,
action] += learning_b * (reward +
discount_b * max(Q[state21, state22]) -
Q[state11, state12, action])
steps -= 1
if back:
M.clear()
if temp > TEMP_MIN:
temp -= decay
tot_episodes += 1
# Track mean length of episodes and mean cumulative reward
test_len = (test_len * (tot_episodes - 1) + steps) / tot_episodes
test_cumr = (test_cumr *
(tot_episodes - 1) + tot_reward) / tot_episodes
env.close()
return learn_len, learn_cumr, test_len, test_cumr
tot_episodes = 0
reached = 0
# Run Enhanced SARSA algorithm: Learning phase
while reached < 40:
# Initialize parameters
done = False
tot_reward, reward = 0,0
steps = 0
pos, vel = test_st[reached]
state = env.reset(pos, vel)
exp = 0.0
M = list()
temp = pow(10, HIGH)
# Discretize state
state_adj = discretize(state)
state_tup = (state_adj[0], state_adj[1])
# Determine the action - Boltzmann policy
action = boltz_policy(state_adj[0], state_adj[1], Q, temp)
while done != True:
# Keep track of visiting number
if state_tup in visited.keys():
visited[state_tup] += 1
else:
visited[state_tup] = 1
if steps == 0:
state_prev = state_adj
def esl(env, learn_st, test_st, alpha=0.9, discount=0.99, back=False):
# Initialize Q table
Q = np.random.uniform(low = -1, high = 1,
size = (11, 11,
env.action_space.n))
learning_b = 0.05
discount_b = 0.99
visited = dict()
exp_weight = 0.7
a_c = 10
a_max = alpha
# thresholds for q~ and exp
q_thresh = 0.1
exp_thresh = -4.5
# Initialize variables to track rewards
learn_len = 0
learn_cumr = 0
tot_episodes = 0
reached = 0
# Run Enhanced SARSA algorithm: Learning phase
while reached < 300:
# Initialize parameters
done = False
tot_reward, reward = 0,0
steps = 0
x = np.random.choice(300)
pos, vel = learn_st[x]
state = env.reset(pos, vel)
exp = 0.0
M = list()
temp = pow(10, HIGH)
# Discretize state
state_adj = discretize(state)
state_tup = (state_adj[0], state_adj[1])
# Determine the action - Boltzmann policy
action = boltz_policy(state_adj[0], state_adj[1], Q, temp)
while done != True:
# Keep track of visiting number
if state_tup in visited.keys():
visited[state_tup] += 1
else:
visited[state_tup] = 1
if steps == 0:
state_prev = state_adj
# compute q~, deltaV and exploration degree
q_th = max(Q[state_adj[0], state_adj[1]]) - min(Q[state_adj[0], state_adj[1]])
delta_v = max(Q[state_adj[0], state_adj[1]]) - max(Q[state_prev[0], state_prev[1]])
exp = exp_weight*exp + (1 - exp_weight)*math.log(temp, 10)
# compute temp using fuzzy balancer
if (q_th < q_thresh and delta_v < 0):
temp = pow(10, LOW)
if (q_th < q_thresh and delta_v > 0 and exp < exp_thresh):
temp = pow(10, VLOW)
if (q_th < q_thresh and delta_v > 0 and exp > exp_thresh):
temp = pow(10, LOW)
if (q_th > q_thresh and delta_v < 0):
temp = pow(10, LOW)
if (q_th > q_thresh and delta_v > 0):
temp = pow(10, LOW)
# Get next state and reward
state2, reward, done, info = env.step(action)
steps += 1
# Discretize state2
state2_adj = discretize(state2)
state2_tup = (state2_adj[0], state2_adj[1])
# Determine next action - Boltzmann policy
action2 = boltz_policy(state_adj[0], state_adj[1], Q, temp)
# Store s, a, r, s' in M
if back:
M.append((state_tup, action, reward, state2_tup))
if done and reward == 200:
reached += 1
# Compute adaptive learning rate
alpha = min(a_c/visited[state_tup], a_max)
# Adjust Q value for current state
else:
delta = alpha*(reward +
discount*Q[state2_adj[0], state2_adj[1], action] -
Q[state_adj[0], state_adj[1],action])
Q[state_adj[0], state_adj[1], action] += delta
# Update variables
tot_reward += reward
state_adj = state2_adj
action = action2
while back and tot_reward > -2500 steps > 0:
state1, action, reward, state2 = M.pop()
state11, state12 = state1
state21, state22 = state2
Q[state11, state12, action] += learning_b*(reward +
discount_b*max(Q[state21, state22]) -
Q[state11, state12, action])
steps -= 1
def adaptive(env, learn_st, test_st, learning=0.5, discount=0.99, temp = TEMP, back=False):
# Initialize Q table
Q = np.random.uniform(low = -1, high = 1,
size = (11, 11,
env.action_space.n))
learning_b = 0.05
discount_b = 0.99
th_p = 100
th_n = -100
c = 50
beta = 2
k = 1.15
discount_init = discount
learning_init = learning
# Initialize variables to track results
learn_len = 0
learn_cumr = 0
tot_episodes = 0
reached = 0
# Run Q learning algorithm
while reached < 300
# Initialize parameters
done = False
tot_reward, reward = 0,0
x = np.random.choice(300)
pos, vel = learn_st[x]
state = env.reset(pos, vel)
steps = 0
M = list()
# Discretize state
state_adj = discretize(state)
state_tup = (state_adj[0], state_adj[1])
while done != True:
# Determine next action - Boltzmann policy
action = boltz_policy(state_adj[0], state_adj[1], Q, temp)
# Get next state and reward
state2, reward, done, info = env.step(action)
steps += 1
# Discretize state2
state2_adj = discretize(state2)
state2_tup = (state2_adj[0], state2_adj[1])
# Store s, a, r, s' in M
if back:
M.append((state_tup, action, reward, state2_tup))
if done and reward == 200:
reached += 1
max_next = max(Q[state2_adj[0], state2_adj[1]])
if ((th_n - k*steps) < max_next) and ((th_p + k*steps) > max_next) :
discount = discount_init
learning = learning_init
else:
discount = np.tanh(steps*discount)
# delta = reward + discount*max_next - Q[state_adj[0], state_adj[1], action]
learning = np.tanh(beta*abs(delta)/steps)
temp = c/steps
# Adjust Q value for current state
else:
delta = learning*(reward +
discount*np.max(Q[state2_adj[0],
state2_adj[1]]) -
Q[state_adj[0], state_adj[1],action])
Q[state_adj[0], state_adj[1],action] += delta
# Update variables
tot_reward += reward
state_adj = state2_adj
while back and tot_reward > -2500 and steps > 0:
state1, action, reward, state2 = M.pop()
state11, state12 = state1
state21, state22 = state2
Q[state11, state12, action] += learning_b*(reward +
discount_b*max(Q[state21, state22]) -
Q[state11, state12, action])
steps -= 1
if back:
M.clear()
tot_episodes += 1
# Track mean length of episodes and mean cumulative reward
learn_len = (learn_len*(tot_episodes - 1) + steps)/tot_episodes
learn_cumr = (learn_cumr*(tot_episodes - 1) + tot_reward)/tot_episodes
