def execute():
"""Execute the learning algorithm"""
s = lp.s
a = lp.a
alpha = lp.alpha
q = lp.q
v = lp.v
policy = lp.policy
agent.execute_action(a)
time.sleep(lp.step_time)
sp = agent.observe_state()
r = agent.obtain_reward(s, a, sp)
ap = agent.select_action(sp)
# update Q
delta = r + exp.GAMMA * q[sp, ap] - q[s, a] # TD error (SARSA)
q[s, a] = q[s, a] + alpha * delta # update rule
# Update V and Policy
v[s] = np.max(q[s])
policy[s] = np.argmax(q[s])
lp.s = s
lp.a = a
lp.sp = sp
lp.ap = ap
lp.r = r
lp.alpha = alpha
lp.q = q
lp.v = v
lp.policy = policy
lp.delta = delta
def execute():
""" Execute Learning Algorithm """
global eligibility, q_old
assert initiated, "TOSL not initiated! setup() must be previously called"
s = lp.s
a = lp.a
alpha = lp.alpha
q = lp.q
v = lp.v
policy = lp.policy
# Specific Learning Algorithm
agent.execute_action(a)
time.sleep(lp.step_time)
# robot.stop()
# time.sleep(TASK.STEP_TIME/2)
sp = agent.observe_state()
r = agent.obtain_reward(s, a, sp)
ap = agent.select_action(sp) # Exploration strategy
diff_q = q[s, a] - q_old
q_old = q[sp, ap]
delta = r + exp.GAMMA * q[sp, ap] - q[s, a] # TD error
eligibility[s, a] = (1.0 - alpha) * eligibility[s, a] + 1
if eli_queue.count(s) > 0:
eli_queue.remove(s)
assert eli_queue.count(s) == 0, ("duplicated states found in ET ", str(s))
eli_queue.appendleft(s)
for i in eli_queue: # no all states updated, just those in eli_queue
# replace eli_queue by range(task.n_states) for non-reduced ET
for j in range(task.n_actions):
if eligibility[i, j] > 0.01:
q[i, j] = q[i, j] + alpha * (delta + diff_q) * eligibility[i, j]
eligibility[i, j] *= exp.GAMMA * exp.LAMBDA
else:
eligibility[i, j] = 0
if i == s and j == a:
q[i, j] = q[i, j] - alpha * diff_q
# update v and policy
v[i] = np.max(q[i])
policy[i] = np.argmax(q[i])
lp.s, lp.a = s, a
lp.sp, lp.ap = sp, ap
lp.r = r
lp.alpha = alpha
lp.q = q
lp.v = v
lp.policy = policy
lp.delta = delta
def test_agent(agent, x_start, y_start, epsilon, goal, pit, labyrinth, plots):
# initialize and plot the environment
state = [x_start, y_start]
env = environment.Environment(x, y, state, goal, pit, labyrinth)
if plots: plot_map(x, y, state, goal, pit, labyrinth, walls, 0)
reward = 0
# run episodes
for step in range(1, 30):
# find state index
state_index = state[0] * y + state[1]
# choose an action
action = agent.select_action(state_index, epsilon)
# the agent moves in the environment
result = env.move(action)
# update state
state = result[0]
reward += result[1]
# plot the environment in the current state
if plots: plot_map(x, y, state, goal, pit, labyrinth, walls, step)
if (state[0] * y == goal[0]) and (state[1] == goal[1]):
print('The agent reached the goal starting from x:', x_start,
' y:', y_start, 'in ', step, ' steps')
break
def execute():
""" Execute the learning algorithm """
global eligibility
assert initiated, " SL not initiated! setup() must be previously called"
s = lp.s
a = lp.a
alpha = lp.alpha
q = lp.q
v = lp.v
policy = lp.policy
# Specific Learning Algorithm
agent.execute_action(a)
time.sleep(lp.step_time)
sp = agent.observe_state()
r = agent.obtain_reward(s, a, sp)
ap = agent.select_action(sp) # Exploration strategy
delta = r + exp.GAMMA * q[sp, ap] - q[s, a] # TD error
eligibility[s, a] = 1.0 # replace trace
if eli_queue.count(s) > 0:
eli_queue.remove(s)
assert eli_queue.count(s) == 0, ("duplicated states found in ET: ", str(s))
eli_queue.appendleft(s)
# only the states in eli_queue are updated:
for i in eli_queue: # no all states updated, just those in eli_queue
# replace eli_queue by range(task.n_states) for non-reduced ET
for j in range(task.n_actions):
if eligibility[i, j] > 0.01:
q[i, j] = q[i, j] + alpha * delta * eligibility[i, j]
eligibility[i, j] *= exp.GAMMA * exp.LAMBDA
else:
eligibility[i, j] = 0
# update v and policy
v[i] = np.max(q[i])
policy[i] = np.argmax(q[i])
lp.s = s
lp.a = a
lp.sp = sp
lp.ap = ap
lp.r = r
lp.alpha = alpha
lp.q = q
lp.v = v
lp.policy = policy
lp.delta = delta
def train(env, n_episodes, render=False):
scores = [] # list containing scores from each episode
scores_window = deque(maxlen=100) # last 100 scores
score = 0
for episode in range(n_episodes):
action_info = env.reset(arenas_configurations_input=arena_config_in)
obs = action_info[brain_name].visual_observations[0][0]
state = get_state(obs)
#total_reward = 0.0
for t in range(100):
action = agent.select_action(state)
conv_action = convert_action(action)
action_info = env.step(conv_action)
obs = action_info[brain_name].visual_observations[0][0]
reward = action_info[brain_name].rewards[0]
score += reward
done = action_info[brain_name].local_done[0]
#total_reward += reward
if not done:
next_state = get_state(obs)
else:
next_state = None
reward = torch.tensor([reward], device=device)
agent.memory.push(state, action.to('cpu'), next_state,
reward.to('cpu'))
state = next_state
if agent.steps_done > INITIAL_MEMORY:
agent.optimize_model()
if agent.steps_done % TARGET_UPDATE == 0:
agent.target_net.load_state_dict(
agent.policy_net.state_dict())
if done:
break
scores_window.append(score)
scores.append(score)
if episode % 20 == 0:
#print('Total steps: {} \t Episode: {}\t Total reward: {}'.format(agent.steps_done, episode, total_reward))
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
episode, np.mean(scores_window)))
env.close()
return
def validation_agent(agent, epsilon, episode_length, goal, pit, labyrinth,
walls, x, y):
avg_val = []
for index in range(0, 100):
# start from a random state not on the walls if inside the labyrinth
if labyrinth:
initial = [np.random.randint(0, x), np.random.randint(0, y)]
for i in walls:
yy = i % 10
xx = i // 10
if initial[0] == xx and initial[1] == yy:
initial = start_walls(initial, walls, x, y)
else:
# start from a random state
initial = [np.random.randint(0, x), np.random.randint(0, y)]
# initialize environment
state = initial
env = environment.Environment(x, y, state, goal, pit, labyrinth)
val_reward = 0
# run episode
for step in range(0, episode_length):
# find state index
state_index = state[0] * y + state[1]
# choose an action
action = agent.select_action(state_index, epsilon)
# the agent moves in the environment
result = env.move(action)
# update state and reward
val_reward += result[1]
state = result[0]
val_reward /= episode_length
avg_val.append(val_reward)
if (index + 1) % 50 == 0:
print('Episode ', index + 1,
': the agent has obtained an average reward of ', val_reward,
' starting from position ', initial)
return np.mean(avg_val)
dim=1)[0].eq(0).type(torch.bool)
non_final_state_locations = (final_state_locations == False)
non_final_states = next_states[non_final_state_locations]
batch_size = next_states.shape[0]
values = torch.zeros(batch_size).to(QValues.device)
values[non_final_state_locations] = target_net(non_final_states).max(
dim=1)[0].detach()
return values
for episode in range(NUM_EPISODES):
env_manage.reset()
state = env_manage.get_state()
for step in count():
action = agent.select_action(state, policy_net)
reward = env_manage.take_action(action)
next_state = env_manage.get_state()
memory.push(tools.Experience(state, action, next_state, reward))
state = next_state
experiences = memory.sample(BATCH_SIZE)
if experiences:
states, actions, rewards, next_states = extract_tensors(
experiences)
q_values = QValues.get_current(policy_net, states, actions)
next_q_values = QValues.get_next(target_net, next_states)
optimal_q_values = (next_q_values * GAMMA) + rewards
loss = F.mse_loss(q_values, optimal_q_values.unsqueeze(1))
agent = dill.load(agent_file)
episodes = 5000
episode_length = 50
epsilon = np.linspace(0.1, 0.001, 5000)
# Perform actions
actions = []
reward = 0
# run episode
for step in range(0, episode_length):
# find state index
state_index = state[0] * 10 + state[1]
#print('State index:', state_index)
# choose an action
action = agent.select_action(state_index, epsilon[-1])
#print('Action:', action)
# the agent moves in the environment
result = env.move(action, verbose=False)
#print('Result:', result)
# Q-learning update
next_index = result[0][0] * 10 + result[0][1]
reward += result[1]
actions.append(env.matrix.copy())
#print('----------')
state = result[0]
# Show final status
im = matpl.plot(env.matrix, env, figsize=(6, 6), reduct=True)
matpl.add_patches(im, env)
