def evaluate_inferred_reward(reward_irl, inferred_rewards, image_irl, start_states_irl, horizon):
"""
Calculates 1-%regret for each (reward, inferred_reward) pair. Regret is amt of reward
lost by planning with the inferred reward, rather than planning with the true reward.
reward_irl(list): list of true rewards (n, imsize, imsize)
inferred_rewards(list): list of inferred rewards (n, imsize, imsize)
image_irl(list): list of walls for each grid (n, imsize, imsize):
start_states_irl(list): list of start states (n, 2) where each entry of form [x,y]
horizon(integer): number of steps to evaluate inferred reward
Returns 1-%regret
"""
reward_percents = []
for label, reward, wall, start_state, i in zip(reward_irl, inferred_rewards, image_irl, start_states_irl, range(len(reward_irl))):
if i < 10:
plot_reward(label, reward, wall, 'reward_pics/reward_{}'.format(i))
percent = evaluate_proxy(wall, start_state, reward, label, episode_length=horizon)
print("Reward had: {}".format(percent))
reward_percents.append(percent)
average_percent_reward = float(sum(reward_percents)) / len(reward_percents)
print(reward_percents[:10])
print('On average planning with the inferred rewards is '
+ str(100 * average_percent_reward)
+ '% as good as planning with the true rewards')
return average_percent_reward
def main(_):
env_name = XMAGICAL_EMBODIMENT_TO_ENV_NAME[FLAGS.embodiment]
env = utils.make_env(env_name, seed=0)
# Reward learning wrapper.
if FLAGS.config.reward_wrapper.pretrained_path is not None:
env = utils.wrap_learned_reward(env, FLAGS.config)
viewer = KeyboardEnvInteractor(action_dim=env.action_space.shape[0])
env.reset()
obs = env.render("rgb_array")
viewer.imshow(obs)
i = [0]
rews = []
def step(action):
obs, rew, done, info = env.step(action)
rews.append(rew)
if obs.ndim != 3:
obs = env.render("rgb_array")
if done:
print(f"Done, score {info['eval_score']:.2f}/1.00")
print("Episode metrics: ")
for k, v in info["episode"].items():
print(f"\t{k}: {v}")
if FLAGS.exit_on_done:
return
i[0] += 1
return obs
viewer.run_loop(step)
utils.plot_reward(rews)
def dqfd_test(exp, agent):
out_filename_format = '_imagetest/episode_{:0>4d}/{:s}/{:0>6d}'
overall_reward = []
for i_episode in range(config.TEST_EPISODE):
print("Environment reset...")
exp.reset()
state = None
meas = None
next_state = None
next_meas = None
offroad_list = []
otherlane_list = []
episode_reward = 0
# transition_queue = collections.deque(maxlen=config.TRAJECTORY_NUM)
for steps in range(config.REPLAY_FRAME):
print("Test episode: %d, frame: %d , length of replaymemory %d" %
(i_episode, steps, len(agent.replay_memory)))
action_no = agent.e_greedy_select_action(state)
action = exp.reverse_action(action_no)
next_meas, next_state, reward, done, _ = exp.step(action)
next_state = utils.rgb_image_to_tensor(next_state['CameraRGB'])
offroad_list.append(next_meas['offroad'])
otherlane_list.append(next_meas['other_lane'])
episode_reward += reward
# reset the enviroment if the car stay offroad or other_lane for 5 consequent steps
if len(offroad_list) > 10:
ar = np.array(offroad_list[-5:]).astype('int64')
tag1 = np.bitwise_and.reduce(ar)
br = np.array(otherlane_list[-10:]).astype('int64')
tag2 = np.bitwise_and.reduce(br)
tag = tag1 | tag2
if tag:
break
print("Reset because of off road or intersection!")
for name, images in exp.cur_image.items():
filename = out_filename_format.format(i_episode, name, steps)
images.save_to_disk(filename)
state = next_state
meas = next_meas
overall_reward.append(episode_reward / steps)
utils.plot_reward(overall_reward)
print("Episode finished!")
print("Test finished!")
return overall_reward
def show_agents(grids,
agent_list,
agent_names,
grid_names,
filename='AgentComparison',
figtitle=''):
"""Shows how agents perform on a gridworld
grid - list of gridworlds (see examples in earlier part of file)
agent_list - list of agent (objects)
agent_names - names of agents (strings)
"""
num_ex = len(agent_list)
num_grids = len(grids)
fig, axes_grid = plt.subplots(num_grids, num_ex, figsize=(14.0, 4.5))
if num_grids == 1:
axes_grid = [axes_grid]
for i, axes in enumerate(axes_grid):
# Give each gridworld a name (uncomment to do so)
# ax.set_ylabel(grid_names[i])
# Generate MDP
grid = grids[i]
mdp = GridworldMdp(grid, noise=0.2)
walls, reward, start = mdp.convert_to_numpy_input()
for idx, agent in enumerate(agent_list):
ax = axes[idx]
ax.set_aspect('equal')
plot_reward(reward, walls, '', fig=fig, ax=ax)
plot_trajectory(walls,
reward,
start,
agent,
arrow_width=0.35,
fig=fig,
ax=ax)
# Only write Agent names if it's the first row
if i == 0:
ax.set_title(agent_names[idx],
fontname='Times New Roman',
fontsize=16)
print('Agent {} is {}'.format(agent_names[idx], agent))
# Increase vertical space btwn subplots
# fig.subplots_adjust(hspace=0.2)
# fig.suptitle(figtitle)
fig.savefig(filename, bbox_inches='tight', dpi=500)
print("Saved figure to {}.png".format(filename))
def random_gridworld_plot(agent, other_agent, size, filename='RandomGrid'):
"""Plots random gridworld"""
from gridworld.gridworld import Direction
from utils import Distribution
if agent is None:
raise ValueError("agent cannot be None")
num_R = 5
mdp = GridworldMdp.generate_random_connected(size, size, num_R, noise=0)
walls, reward, start = mdp.convert_to_numpy_input()
def get_policy(agent):
num_actions = 5
imsize = len(walls)
def dist_to_numpy(dist):
return dist.as_numpy_array(Direction.get_number_from_direction,
num_actions)
def action(state):
# Walls are invalid states and the MDP will refuse to give an action for
# them. However, the VIN's architecture requires it to provide an action
# distribution for walls too, so hardcode it to always be STAY.
x, y = state
if mdp.walls[y][x]:
return dist_to_numpy(Distribution({Direction.STAY: 1}))
return dist_to_numpy(agent.get_action_distribution(state))
agent.set_mdp(mdp)
action_dists = [[action((x, y)) for x in range(imsize)]
for y in range(imsize)]
action_dists = np.array(action_dists)
return action_dists
fig, axes = plt.subplots(1, 1)
fig.set_size_inches(5, 5)
# Reward only
plot_reward(reward, np.zeros_like(walls), fig=fig, ax=axes, ax_title='')
fig.savefig(filename + 'R', bbox_inches='tight', dpi=100)
# Walls only
plot_reward(np.zeros_like(reward), walls, fig=fig, ax=axes, ax_title='')
fig.savefig(filename + 'W', bbox_inches='tight', dpi=100)
# Trajectory + Walls + Rewards
plot_reward(reward, walls, fig=fig, ax=axes, ax_title='')
# plot_trajectory(walls, reward, start, agent, fig=fig, ax=axes)
policy = get_policy(agent)
plot_policy(walls, policy, fig=fig, ax=axes)
fig.savefig(filename + 'Ptrue', bbox_inches='tight', dpi=100)
axes.clear()
plot_reward(reward, walls, fig=fig, ax=axes, ax_title='')
predicted = get_policy(other_agent)
plot_policy_diff(predicted, policy, walls, fig=fig, ax=axes)
fig.savefig(filename + 'Ppredicted', bbox_inches='tight', dpi=100)
accumulated_reward += state.reward
accumulated_reward_list.append(accumulated_reward)
if opt.disp_on:
if win_all is None:
plt.subplot(121)
win_all = plt.imshow(state.screen)
plt.subplot(122)
win_pob = plt.imshow(state.pob)
else:
win_all.set_data(state.screen)
win_pob.set_data(state.pob)
plt.pause(opt.disp_interval)
plt.draw()
epi_step = epi_step + 1
if step % 100:
print("Step {}: Accumulated reward= {}".format(step,
accumulated_reward))
elapsed_time = time() - start_time
print("----- Finished testing -----")
print("Completed {} games, reached targt {} times".format(
opt.steps, n_completed_episodes))
print("{}% of games the target was reached".format(n_completed_episodes /
opt.steps))
print("Total time : {:.2f} seconds".format(elapsed_time))
print("Accumulated reward: ", accumulated_reward_list[-1])
plot_reward(accumulated_reward_list, opt.acc_reward_test_figure_path)
test(env, likelihood)
env.adapt_a()
likelihood.reinitialize_optimizer(lr=1e-2)
l_a2b, l_b2a = control.train_with_buffer(
env, likelihood, hparam['nb_episode_adapt'])
likelihood_a2b_adapt[run, :] = l_a2b
likelihood_b2a_adapt[run, :] = l_b2a
utils.plot_training(likelihood_a2b, likelihood_b2a, hparam['output'], True)
utils.plot_adaptation(likelihood_a2b_adapt, likelihood_b2a_adapt,
hparam['output'], True)
if rl_mode:
reward_a2b = np.cumsum(reward_a2b, axis=1)
reward_b2a = np.cumsum(reward_b2a, axis=1)
reward_a2b_adapt = np.cumsum(reward_a2b_adapt, axis=1)
reward_b2a_adapt = np.cumsum(reward_b2a_adapt, axis=1)
utils.plot_reward(reward_a2b, reward_b2a, hparam['output'], True)
utils.plot_reward_adapt(reward_a2b_adapt, reward_b2a_adapt,
hparam['output'], True)
def test(env, likelihood):
env.compare_directions(likelihood)
env.compare_likelihood(likelihood, 0, 0, 0, 0, 0)
env.compare_likelihood(likelihood, 1, 0, 0, 0, 0)
env.compare_likelihood(likelihood, 2, 0, 0, 0, 0)
env.compare_likelihood(likelihood, 3, 0, 0, 0, 0)
__import__('ipdb').set_trace()
def run_game(self):
config = self.config
n = config.runs_per_agent
prev_best_reward = -1000
for run in range(n):
# potentially, we can change the goals as agent picks up more skills
env = eval(config.environment)
test_env = eval(config.environment)
cLoss, aLoss = [], []
# 0. instantiate an agent instance of this class
agent = self.agentCls(**self.agentArgs)
obs_dim, act_dim = agent.obs_dim, agent.act_dim
# 1. instantiate a memory pool and warm up
rpm = ReplayMemory(config.memory_size, obs_dim, act_dim)
# 2. set up logging file
save_dir = config.log_path + "{}_{}".format(self.name, run + 1)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# 3. start training
test_flag, total_steps = 0, 0
train_rewards, test_means, test_stds = [], [], []
pbar = tqdm(total=config.train_total_steps)
while total_steps < config.train_total_steps:
para = [
config.reward_scale, config.warmup_size, config.batch_size,
config.expl_noise
]
train_reward, steps, costC, costA = run_train_episode(
env, agent, rpm, *para)
total_steps += steps
train_rewards.append(train_reward)
cLoss.append(costC)
aLoss.append(costA)
pbar.set_description('Steps: {} Reward: {}'.format(
total_steps, train_reward))
pbar.update(steps)
# 4. start testing
if total_steps // config.test_every_steps >= test_flag:
while total_steps // config.test_every_steps >= test_flag:
test_flag += 1
r_mean, r_std = run_evaluate_episode(test_env, agent)
logger.info('Steps {}, Evaluate reward: {}'.format(
total_steps, r_mean))
test_means.append(r_mean)
test_stds.append(r_std)
if config.save_model and r_mean > prev_best_reward:
prev_best_reward = r_mean
ckpt = save_dir + '/Steps_{}_reward_{}.ckpt'.format(
total_steps, int(r_mean))
agent.save(ckpt, program=agent.pred_program)
np.savez(save_dir + '/record.npz',
train=train_rewards,
mean=test_means,
std=test_stds,
closs=cLoss,
aloss=aLoss)
if config.visual_result:
plot_reward(train_rewards)
plot_reward(test_means, test_stds)
'-------------------------------------------------'
)
subtask_list.append(subtask_index)
subtask_reward.append(acc_reward)
task_r += acc_reward
if render:
task[subtask_index].close()
task_reward.append(task_r)
time_task = time.time() - time_task_0
if task_epi % 1 == 0:
# record the reward
plot_reward(subtask_list,
subtask_reward,
name=model.name + '_' +
'subtask_reward_CartPole_SwingUp_' + str(task_epi),
xlabel='subtask',
y=195)
plot_reward(range(len(task_reward)),
task_reward,
name=model.name + '_' +
'task_reward_CartPole_SwingUp_' + str(task_epi),
xlabel='episode',
y=1560,
scatter=False)
print('***************************')
print('task_episode: ', task_epi, ' time: ', time_task)
if model.name == 'DPGPMM':
numbers = []
for comp in model.DP_mix.comps:
'optimizer_actor_state_dict':
agent.optimizer_actor.state_dict(),
'optimizer_critic_state_dict':
agent.optimizer_actor.state_dict()
}, 'agent_state_dict.pt')
if np.mean(scores_window) >= 32:
print("\n Problem Solved!")
break
print("Score: {}".format(score))
#Plotting Rewards
plot_reward(scores)
############################## TESTING AGENT ########################################################
#####################################################################################################
# Loading saved parameters to test agent over 100 trials
checkpoint = torch.load('agent_state_dict.pt', map_location="cpu")
agent.actor_main.load_state_dict(checkpoint['actor_main_network_state_dict'])
agent.critic_main.load_state_dict(checkpoint['critic_main_network_state_dict'])
def test(num_episodes=100):
all_scores = []
from tqdm import tqdm
config = init_birl_flags()
if config.datafile is None:
print('--datafile option is required')
exit()
# seed random generators
set_seeds(config.seed)
imagetest, rewardtest, ytest = load_dataset(config.datafile)[-3:]
for image, reward, policy in zip(imagetest, rewardtest, ytest):
mdp = GridworldMdp.from_numpy_input(image, reward)
mdp = GridworldMdpLearnableR.from_full_mdp(mdp)
inferred_reward = birl(mdp,
policy,
config.beta,
num_burn_in=config.num_burn_in,
num_samples=config.num_samples,
display_step=config.display_step)
print('The first set of walls is:')
print(image)
print('The first reward should be:')
print(reward)
inferred_reward = inferred_reward / inferred_reward.max()
inferred_reward = np.reshape(inferred_reward, image.shape)
print('The inferred reward is:')
print(inferred_reward)
plot_reward(reward, inferred_reward)
break
def dqfd_replay(exp, agent):
overall_reward = []
for i_episode in range(config.REPLAY_EPISODE):
print("Environment reset...")
exp.reset()
state = None
meas = None
next_state = None
next_meas = None
offroad_list = []
otherlane_list = []
episode_reward = 0
# transition_queue = collections.deque(maxlen=config.TRAJECTORY_NUM)
for steps in itertools.count(config.DEMO_BUFFER_SIZE):
frame_no = steps - config.DEMO_BUFFER_SIZE
print("Replay episode: %d, frame: %d , length of replaymemory %d" %
(i_episode, frame_no, len(agent.replay_memory)))
action_no = agent.e_greedy_select_action(state)
action = exp.reverse_action(action_no)
next_meas, next_state, reward, done, _ = exp.step(action)
next_state = utils.rgb_image_to_tensor(next_state['CameraRGB'])
offroad_list.append(next_meas['offroad'])
otherlane_list.append(next_meas['other_lane'])
episode_reward += reward
# reset the enviroment if the car stay offroad or other_lane for 5 consequent steps
if len(offroad_list) > 10:
ar = np.array(offroad_list[-5:]).astype('int64')
tag1 = np.bitwise_and.reduce(ar)
br = np.array(otherlane_list[-10:]).astype('int64')
tag2 = np.bitwise_and.reduce(br)
tag = tag1 | tag2
if tag:
exp.reset()
if meas:
transition = Transition(
meas, state, torch.tensor([[action_no]]),
torch.tensor([[reward]]), next_state, next_meas,
torch.zeros(1)
) # TODO: use both the measurement and the image later
agent.replay_memory_push([transition])
state = next_state
meas = next_meas
if agent.replay_memory.is_full:
print("Trainning!")
agent.train()
#
# if done:
# print("episode: %d, memory length: %d epsilon: %f" % (i_episode, len(agent.replay_memory), agent.epsilon))
# break
if steps % 100 == 0:
agent.update_target_net()
if frame_no >= config.REPLAY_FRAME:
overall_reward.append(episode_reward / config.REPLAY_FRAME)
utils.plot_reward(overall_reward)
print("Episode finished!")
break
#save the result every 20 episode
if i_episode % 20 == 0:
print("Saving prameters for the last 20 episodes")
reward_df = pd.DataFrame(overall_reward)
reward_df.to_csv('_episode_reward%d.csv' % i_episode)
with open(config.CARLA_TRAIN_FILE, 'wb') as f:
pickle.dump(agent, f)
print("Trained parameters achevied!")
print("Replay finished!")
return overall_reward
