def default_hud_data_from_trajectories(trajectories, trajectory_idx=0):
scores = cumulative_rewards_from_rew_list(
trajectories["ep_rewards"][trajectory_idx])
return [
StateVisualizer.default_hud_data(state, score=scores[i]) for i,
state in enumerate(trajectories["ep_states"][trajectory_idx])
]
def interactive_from_traj(trajectories, traj_idx=0):
"""
Displays ith trajectory of trajectories (in standard format)
interactively in a Jupyter notebook.
"""
from ipywidgets import widgets, interactive_output
states = trajectories["ep_observations"][traj_idx]
joint_actions = trajectories["ep_actions"][traj_idx]
cumulative_rewards = cumulative_rewards_from_rew_list(
trajectories["ep_rewards"][traj_idx])
mdp_params = trajectories["mdp_params"][traj_idx]
env_params = trajectories["env_params"][traj_idx]
env = AgentEvaluator(mdp_params, env_params=env_params).env
def update(t=1.0):
env.state = states[int(t)]
joint_action = joint_actions[int(t -
1)] if t > 0 else (Action.STAY,
Action.STAY)
print(env)
print("Joint Action: {} \t Score: {}".format(
Action.joint_action_to_char(joint_action),
cumulative_rewards[t]))
t = widgets.IntSlider(min=0, max=len(states) - 1, step=1, value=0)
out = interactive_output(update, {'t': t})
display(out, t)
def interactive_from_traj(trajectories,
traj_idx=0,
nested_keys_to_print=[]):
"""
Displays ith trajectory of trajectories (in standard format)
interactively in a Jupyter notebook.
keys_to_print is a list of keys of info to be printed. By default
states and actions (corresponding to the previous timestep will be printed)
will be printed.
"""
from ipywidgets import widgets, interactive_output
states = trajectories["ep_states"][traj_idx]
joint_actions = trajectories["ep_actions"][traj_idx]
other_info = {}
for nested_k in nested_keys_to_print:
inner_data = trajectories
for k in nested_k:
inner_data = inner_data[k]
inner_data = inner_data[traj_idx]
assert np.array(inner_data).shape == np.array(
states).shape, "{} vs {}".format(
np.array(inner_data).shape,
np.array(states).shape)
other_info[k] = inner_data
cumulative_rewards = cumulative_rewards_from_rew_list(
trajectories["ep_rewards"][traj_idx])
mdp_params = trajectories["mdp_params"][traj_idx]
env_params = trajectories["env_params"][traj_idx]
env = AgentEvaluator(mdp_params, env_params=env_params).env
def update(t=1.0):
traj_timestep = int(t)
env.state = states[traj_timestep]
joint_action = joint_actions[traj_timestep -
1] if traj_timestep > 0 else (
Action.STAY, Action.STAY)
print(env)
print("Joint Action: {} \t Score: {}".format(
Action.joint_action_to_char(joint_action),
cumulative_rewards[t]))
for k, data in other_info.items():
print("{}: {}".format(k, data[traj_timestep]))
t = widgets.IntSlider(min=0, max=len(states) - 1, step=1, value=0)
out = interactive_output(update, {'t': t})
display(out, t)
def on_trajectory_selected(data):
traj_idx = int(data["trajectory_idx"] or 0)
trajectory_path = os.path.join(TRAJECTORIES_DIR, data["trajectory_file"])
trajectories = AgentEvaluator.load_traj_from_json(trajectory_path)
trajectories_json = load_from_json(trajectory_path)
trajectory_states = trajectories_json["ep_states"][traj_idx]
trajectory_rewards = trajectories_json["ep_rewards"][traj_idx]
scores = cumulative_rewards_from_rew_list(trajectory_rewards)
states = [{"state":state, "time_left": time_left, "score": score} for state, score, time_left in zip(trajectory_states, scores, reversed(range(len(trajectory_states))))]
terrain = trajectories_json["mdp_params"][traj_idx]["terrain"]
settings = copy.deepcopy(DEFAULT_EVENT_CHART_SETTINGS)
settings["show_cumulative_data"] = False
settings["chart_box_view_box"] = "0 0 800 150"
start_info = {
"terrain": terrain,
"state": states[0]
}
socketio.emit("replay_trajectory", {"start_info": start_info,
"states": states,
"max_trajectory_idx": len(trajectories["ep_states"])-1,
"trajectory_chart_events": extract_events(trajectories, traj_idx),
"trajectory_chart_settings": settings})