diff --git a/configs/tasks/obj_nav_mp3d.yaml b/configs/tasks/obj_nav_mp3d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bdf61f0288ed67c516a28e2f7334a93056c9b63d
--- /dev/null
+++ b/configs/tasks/obj_nav_mp3d.yaml
@@ -0,0 +1,47 @@
+ENVIRONMENT:
+ MAX_EPISODE_STEPS: 500
+SIMULATOR:
+ TURN_ANGLE: 30
+ TILT_ANGLE: 30
+ AGENT_0:
+ SENSORS: ['RGB_SENSOR', 'DEPTH_SENSOR']
+ HEIGHT: 0.88
+ RADIUS: 0.2
+ HABITAT_SIM_V0:
+ GPU_DEVICE_ID: 0
+ SEMANTIC_SENSOR:
+ WIDTH: 640
+ HEIGHT: 480
+ HFOV: 79
+ POSITION: [0, 0.88, 0]
+ RGB_SENSOR:
+ WIDTH: 640
+ HEIGHT: 480
+ HFOV: 79
+ POSITION: [0, 0.88, 0]
+ DEPTH_SENSOR:
+ WIDTH: 640
+ HEIGHT: 480
+ HFOV: 79
+ MIN_DEPTH: 0.5
+ MAX_DEPTH: 5.0
+ POSITION: [0, 0.88, 0]
+TASK:
+ TYPE: ObjectNav-v1
+ SUCCESS_DISTANCE: 0.1
+
+ SENSORS: ['OBJECTGOAL_SENSOR']
+ GOAL_SENSOR_UUID: objectgoal
+
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
+ SPL:
+ TYPE: SPL
+ DISTANCE_TO: VIEW_POINTS
+ SUCCESS_DISTANCE: 0.2
+
+DATASET:
+ TYPE: ObjectNav-v1
+ SPLIT: val
+ CONTENT_SCENES: []
+ DATA_PATH: "data/datasets/objectnav/mp3d/v1/{split}/{split}.json.gz"
+ SCENES_DIR: "data/scene_datasets/"
diff --git a/configs/tasks/objectnav_mp3d.yaml b/configs/tasks/objectnav_mp3d.yaml
index 1289ceebb7b61553139cf5c2bd9c9b165f94abb7..8811ba838a4d7fc8af95418b250041f0f6700332 100644
--- a/configs/tasks/objectnav_mp3d.yaml
+++ b/configs/tasks/objectnav_mp3d.yaml
@@ -3,6 +3,7 @@ ENVIRONMENT:
SIMULATOR:
TURN_ANGLE: 30
TILT_ANGLE: 30
+ ACTION_SPACE_CONFIG: "v1"
AGENT_0:
SENSORS: ['RGB_SENSOR', 'DEPTH_SENSOR']
HEIGHT: 0.88
@@ -28,16 +29,19 @@ SIMULATOR:
POSITION: [0, 0.88, 0]
TASK:
TYPE: ObjectNav-v1
+ POSSIBLE_ACTIONS: ["STOP", "MOVE_FORWARD", "TURN_LEFT", "TURN_RIGHT", "LOOK_UP", "LOOK_DOWN"]
SUCCESS_DISTANCE: 0.1
- SENSORS: ['OBJECTGOAL_SENSOR']
+ SENSORS: ['OBJECTGOAL_SENSOR', 'COMPASS_SENSOR', 'GPS_SENSOR']
GOAL_SENSOR_UUID: objectgoal
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
DISTANCE_TO: VIEW_POINTS
SUCCESS_DISTANCE: 0.2
+ DISTANCE_TO_GOAL:
+ DISTANCE_TO: VIEW_POINTS
DATASET:
TYPE: ObjectNav-v1
diff --git a/configs/tasks/pointnav.yaml b/configs/tasks/pointnav.yaml
index bcf81eaec4bfe237c7aa0086d029f6a4655d6c76..0f60544a723184a5a3f8b2b5f5eb2092db6758a1 100644
--- a/configs/tasks/pointnav.yaml
+++ b/configs/tasks/pointnav.yaml
@@ -21,7 +21,7 @@ TASK:
DIMENSIONALITY: 2
GOAL_SENSOR_UUID: pointgoal_with_gps_compass
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 0.2
diff --git a/configs/tasks/pointnav_gibson.yaml b/configs/tasks/pointnav_gibson.yaml
index 52d5ae7082f61a44056cbc42fc57530c788f2fba..729c64c905d972c730761e1903b696096c654d33 100644
--- a/configs/tasks/pointnav_gibson.yaml
+++ b/configs/tasks/pointnav_gibson.yaml
@@ -21,7 +21,7 @@ TASK:
DIMENSIONALITY: 2
GOAL_SENSOR_UUID: pointgoal_with_gps_compass
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 0.2
diff --git a/configs/tasks/pointnav_mp3d.yaml b/configs/tasks/pointnav_mp3d.yaml
index 341cac7e6d1cedb7bba4c089f3f7a330993cf9a0..1c53200beeb88ba0f5389f71c0587fe14b80b62d 100644
--- a/configs/tasks/pointnav_mp3d.yaml
+++ b/configs/tasks/pointnav_mp3d.yaml
@@ -21,7 +21,7 @@ TASK:
DIMENSIONALITY: 2
GOAL_SENSOR_UUID: pointgoal_with_gps_compass
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 0.2
diff --git a/configs/tasks/pointnav_rgbd.yaml b/configs/tasks/pointnav_rgbd.yaml
index e1660792c4cf41760081209397c26c0adf0c195a..5c74f537ddafb47edf2ec6bd04bfaf685c80d8f9 100644
--- a/configs/tasks/pointnav_rgbd.yaml
+++ b/configs/tasks/pointnav_rgbd.yaml
@@ -21,7 +21,7 @@ TASK:
DIMENSIONALITY: 2
GOAL_SENSOR_UUID: pointgoal_with_gps_compass
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 0.2
diff --git a/configs/tasks/vln_r2r.yaml b/configs/tasks/vln_r2r.yaml
index 41873216395a8109a5d091770c2caf883bd753f4..bdb4bbe67343257293be3ce3eeceaa8af961a930 100644
--- a/configs/tasks/vln_r2r.yaml
+++ b/configs/tasks/vln_r2r.yaml
@@ -21,7 +21,7 @@ TASK:
SENSORS: ['INSTRUCTION_SENSOR']
INSTRUCTION_SENSOR_UUID: instruction
POSSIBLE_ACTIONS: ['STOP', 'MOVE_FORWARD', 'TURN_LEFT', 'TURN_RIGHT']
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 3.0
diff --git a/configs/test/habitat_all_sensors_test.yaml b/configs/test/habitat_all_sensors_test.yaml
index d840f48636412636c4b4af25affe78dc1fe455ed..a603475b7f421ecb4a2f0d4c3817b29d2ef5c049 100644
--- a/configs/test/habitat_all_sensors_test.yaml
+++ b/configs/test/habitat_all_sensors_test.yaml
@@ -25,7 +25,7 @@ TASK:
DIMENSIONALITY: 2
GOAL_SENSOR_UUID: pointgoal_with_gps_compass
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 0.2
diff --git a/configs/test/habitat_mp3d_object_nav_test.yaml b/configs/test/habitat_mp3d_object_nav_test.yaml
index 88a5478063003425c3fa8bd1818559fe3ae44af6..163f1a58bcc6a3422c373aa4d90fe4737a7c2957 100644
--- a/configs/test/habitat_mp3d_object_nav_test.yaml
+++ b/configs/test/habitat_mp3d_object_nav_test.yaml
@@ -1,8 +1,9 @@
ENVIRONMENT:
- MAX_EPISODE_STEPS: 750
+ MAX_EPISODE_STEPS: 500
SIMULATOR:
TURN_ANGLE: 30
TILT_ANGLE: 30
+ ACTION_SPACE_CONFIG: "v1"
AGENT_0:
SENSORS: ['RGB_SENSOR', 'DEPTH_SENSOR']
HEIGHT: 0.88
@@ -26,23 +27,25 @@ SIMULATOR:
MIN_DEPTH: 0.5
MAX_DEPTH: 5.0
POSITION: [0, 0.88, 0]
-
TASK:
TYPE: ObjectNav-v1
+ POSSIBLE_ACTIONS: ["STOP", "MOVE_FORWARD", "TURN_LEFT", "TURN_RIGHT", "LOOK_UP", "LOOK_DOWN"]
SUCCESS_DISTANCE: 0.1
- SENSORS: ['OBJECTGOAL_SENSOR']
+ SENSORS: ['OBJECTGOAL_SENSOR', 'COMPASS_SENSOR', 'GPS_SENSOR']
GOAL_SENSOR_UUID: objectgoal
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
DISTANCE_TO: VIEW_POINTS
SUCCESS_DISTANCE: 0.2
+ DISTANCE_TO_GOAL:
+ DISTANCE_TO: VIEW_POINTS
DATASET:
TYPE: ObjectNav-v1
SPLIT: mini_val
CONTENT_SCENES: []
- DATA_PATH: "data/datasets/objectnav/mp3d/v1/{split}/{split}.json.gz"
+ DATA_PATH: "data/datasets/objectnav/mp3d/v0/{split}/{split}.json.gz"
SCENES_DIR: "data/scene_datasets/"
diff --git a/configs/test/habitat_r2r_vln_test.yaml b/configs/test/habitat_r2r_vln_test.yaml
index 7e6fd8511f22274f76c4ab3efbd7f8f7ada588e7..e202568a4bed01f20f93fd0119ecd6b698d7c959 100644
--- a/configs/test/habitat_r2r_vln_test.yaml
+++ b/configs/test/habitat_r2r_vln_test.yaml
@@ -21,7 +21,7 @@ TASK:
DIMENSIONALITY: 2
GOAL_SENSOR_UUID: pointgoal_with_gps_compass
INSTRUCTION_SENSOR_UUID: instruction
- MEASUREMENTS: ['SPL']
+ MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SPL']
SPL:
TYPE: SPL
SUCCESS_DISTANCE: 3.0
diff --git a/habitat/config/default.py b/habitat/config/default.py
index cd4a448b579c5feef9aa5afbe9430915cc83fe11..1182a03e82fdc6c618d820f50a04fd326ffeb8fe 100644
--- a/habitat/config/default.py
+++ b/habitat/config/default.py
@@ -128,7 +128,6 @@ _C.TASK.PROXIMITY_SENSOR.MAX_DETECTION_RADIUS = 2.0
_C.TASK.SPL = CN()
_C.TASK.SPL.TYPE = "SPL"
_C.TASK.SPL.SUCCESS_DISTANCE = 0.2
-_C.TASK.SPL.DISTANCE_TO = "POINT"
# -----------------------------------------------------------------------------
# TopDownMap MEASUREMENT
# -----------------------------------------------------------------------------
@@ -185,6 +184,7 @@ _C.TASK.INSTRUCTION_SENSOR_UUID = "instruction"
# -----------------------------------------------------------------------------
_C.TASK.DISTANCE_TO_GOAL = CN()
_C.TASK.DISTANCE_TO_GOAL.TYPE = "DistanceToGoal"
+_C.TASK.DISTANCE_TO_GOAL.DISTANCE_TO = "POINT"
# -----------------------------------------------------------------------------
# # ANSWER_ACCURACY MEASUREMENT
# -----------------------------------------------------------------------------
diff --git a/habitat/core/embodied_task.py b/habitat/core/embodied_task.py
index aafc7d6a760df5aeb33e02fddc0a6d01f92522db..7a3813c022b354e674dd69714feee3d3789a97e4 100644
--- a/habitat/core/embodied_task.py
+++ b/habitat/core/embodied_task.py
@@ -8,7 +8,7 @@ r"""Implements tasks and measurements needed for training and benchmarking of
"""
from collections import OrderedDict
-from typing import Any, Dict, Iterable, Optional, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Type, Union
import numpy as np
@@ -175,6 +175,31 @@ class Measurements:
"""
return Metrics(self.measures)
+ def _get_measure_index(self, measure_name):
+ return list(self.measures.keys()).index(measure_name)
+
+ def check_measure_dependencies(
+ self, measure_name: str, dependencies: List[str]
+ ):
+ r"""Checks if dependencies measures are enabled and calculatethat the measure
+ :param measure_name: a name of the measure for which has dependencies.
+ :param dependencies: a list of a measure names that are required by
+ the measure.
+ :return:
+ """
+ measure_index = self._get_measure_index(measure_name)
+ for dependency_measure in dependencies:
+ assert (
+ dependency_measure in self.measures
+ ), f"""{measure_name} measure requires {dependency_measure}
+ listed in tje measures list in the config."""
+
+ for dependency_measure in dependencies:
+ assert measure_index > self._get_measure_index(
+ dependency_measure
+ ), f"""{measure_name} measure requires be listed after {dependency_measure}
+ in tje measures list in the config."""
+
class EmbodiedTask:
r"""Base class for embodied tasks. ``EmbodiedTask`` holds definition of
diff --git a/habitat/tasks/nav/nav.py b/habitat/tasks/nav/nav.py
index d7b9e5981e78f7fa6d7bf7edb86ccfef04a3dff5..007ecbc06a41f75f03c07f52516562cfd3069bf0 100644
--- a/habitat/tasks/nav/nav.py
+++ b/habitat/tasks/nav/nav.py
@@ -299,8 +299,13 @@ class EpisodicCompassSensor(HeadingSensor):
rotation_world_agent = agent_state.rotation
rotation_world_start = quaternion_from_coeff(episode.start_rotation)
- return self._quat_to_xy_heading(
- rotation_world_agent.inverse() * rotation_world_start
+ return np.array(
+ [
+ self._quat_to_xy_heading(
+ rotation_world_agent.inverse() * rotation_world_start
+ )
+ ],
+ dtype=np.float32,
)
@@ -407,6 +412,8 @@ class SPL(Measure):
ref: On Evaluation of Embodied Agents - Anderson et. al
https://arxiv.org/pdf/1807.06757.pdf
+ The measure depends on Distance to Goal measure to improve computational
+ performance for sophisticated goal areas.
"""
def __init__(
@@ -424,17 +431,14 @@ class SPL(Measure):
def _get_uuid(self, *args: Any, **kwargs: Any):
return "spl"
- def reset_metric(self, *args: Any, episode, **kwargs: Any):
+ def reset_metric(self, *args: Any, episode, task, **kwargs: Any):
self._previous_position = self._sim.get_agent_state().position.tolist()
self._start_end_episode_distance = episode.info["geodesic_distance"]
self._agent_episode_distance = 0.0
- self._metric = None
- if self._config.DISTANCE_TO == "VIEW_POINTS":
- self._episode_view_points = [
- view_point.agent_state.position
- for goal in episode.goals
- for view_point in goal.view_points
- ]
+ task.measurements.check_measure_dependencies(
+ self.uuid, [DistanceToGoal.cls_uuid]
+ )
+ self.update_metric(*args, episode=episode, task=task, **kwargs)
def _euclidean_distance(self, position_a, position_b):
return np.linalg.norm(
@@ -442,23 +446,13 @@ class SPL(Measure):
)
def update_metric(
- self, *args: Any, episode, action, task: EmbodiedTask, **kwargs: Any
+ self, *args: Any, episode, task: EmbodiedTask, **kwargs: Any
):
ep_success = 0
current_position = self._sim.get_agent_state().position.tolist()
-
- if self._config.DISTANCE_TO == "POINT":
- distance_to_target = self._sim.geodesic_distance(
- current_position, [goal.position for goal in episode.goals]
- )
- elif self._config.DISTANCE_TO == "VIEW_POINTS":
- distance_to_target = self._sim.geodesic_distance(
- current_position, self._episode_view_points
- )
- else:
- logger.error(
- f"Non valid DISTANCE_TO parameter was provided: {self._config.DISTANCE_TO}"
- )
+ distance_to_target = task.measurements.measures[
+ DistanceToGoal.cls_uuid
+ ].get_metric()
if (
hasattr(task, "is_stop_called")
@@ -795,10 +789,11 @@ class TopDownMap(Measure):
@registry.register_measure
class DistanceToGoal(Measure):
- """The measure provides a set of metrics that illustrate agent's progress
- towards the goal.
+ """The measure calculates a distance towards the goal.
"""
+ cls_uuid: str = "distance_to_goal"
+
def __init__(
self, sim: Simulator, config: Config, *args: Any, **kwargs: Any
):
@@ -807,11 +802,12 @@ class DistanceToGoal(Measure):
self._agent_episode_distance = None
self._sim = sim
self._config = config
+ self._episode_view_points = None
super().__init__(**kwargs)
def _get_uuid(self, *args: Any, **kwargs: Any):
- return "distance_to_goal"
+ return self.cls_uuid
def reset_metric(self, episode, *args: Any, **kwargs: Any):
self._previous_position = self._sim.get_agent_state().position.tolist()
@@ -820,18 +816,34 @@ class DistanceToGoal(Measure):
)
self._agent_episode_distance = 0.0
self._metric = None
+ if self._config.DISTANCE_TO == "VIEW_POINTS":
+ self._episode_view_points = [
+ view_point.agent_state.position
+ for goal in episode.goals
+ for view_point in goal.view_points
+ ]
+ self.update_metric(*args, episode=episode, **kwargs)
def _euclidean_distance(self, position_a, position_b):
return np.linalg.norm(
np.array(position_b) - np.array(position_a), ord=2
)
- def update_metric(self, episode, action, *args: Any, **kwargs: Any):
+ def update_metric(self, episode, *args: Any, **kwargs: Any):
current_position = self._sim.get_agent_state().position.tolist()
- distance_to_target = self._sim.geodesic_distance(
- current_position, episode.goals[0].position
- )
+ if self._config.DISTANCE_TO == "POINT":
+ distance_to_target = self._sim.geodesic_distance(
+ current_position, [goal.position for goal in episode.goals]
+ )
+ elif self._config.DISTANCE_TO == "VIEW_POINTS":
+ distance_to_target = self._sim.geodesic_distance(
+ current_position, self._episode_view_points
+ )
+ else:
+ logger.error(
+ f"Non valid DISTANCE_TO parameter was provided: {self._config.DISTANCE_TO}"
+ )
self._agent_episode_distance += self._euclidean_distance(
current_position, self._previous_position
@@ -839,13 +851,7 @@ class DistanceToGoal(Measure):
self._previous_position = current_position
- self._metric = {
- "distance_to_target": distance_to_target,
- "start_distance_to_target": self._start_end_episode_distance,
- "distance_delta": self._start_end_episode_distance
- - distance_to_target,
- "agent_path_length": self._agent_episode_distance,
- }
+ self._metric = distance_to_target
@registry.register_task_action
diff --git a/habitat/tasks/nav/object_nav_task.py b/habitat/tasks/nav/object_nav_task.py
index e8db317419214727a076cb2347d0dfc8ec6c6987..87389448d4585dbdff185327a302ab87a8b39a64 100644
--- a/habitat/tasks/nav/object_nav_task.py
+++ b/habitat/tasks/nav/object_nav_task.py
@@ -109,7 +109,9 @@ class ObjectGoalSensor(Sensor):
sensor_shape = (1,)
max_value = (self.config.GOAL_SPEC_MAX_VAL - 1,)
if self.config.GOAL_SPEC == "TASK_CATEGORY_ID":
- max_value = len(self._dataset.category_to_task_category_id)
+ max_value = max(
+ self._dataset.category_to_task_category_id.values()
+ )
return spaces.Box(
low=0, high=max_value, shape=sensor_shape, dtype=np.int64
@@ -134,7 +136,10 @@ class ObjectGoalSensor(Sensor):
)
return None
category_name = episode.goals[0].object_category
- return self._dataset.category_to_task_category_id[category_name]
+ return np.array(
+ [self._dataset.category_to_task_category_id[category_name]],
+ dtype=np.int64,
+ )
elif self.config.GOAL_SPEC == "OBJECT_ID":
return np.array([episode.goals[0].object_name_id], dtype=np.int64)
else:
diff --git a/habitat_baselines/common/environments.py b/habitat_baselines/common/environments.py
index b7d43dd888a8f60fe0084b40058460f3d199dd66..99eeb14080567526da13228e88a165cee1fd1c81 100644
--- a/habitat_baselines/common/environments.py
+++ b/habitat_baselines/common/environments.py
@@ -10,7 +10,7 @@ in habitat. Customized environments should be registered using
``@baseline_registry.register_env(name="myEnv")` for reusability
"""
-from typing import Any, Dict, Optional, Type, Union
+from typing import Optional, Type
import habitat
from habitat import Config, Dataset
@@ -34,20 +34,18 @@ class NavRLEnv(habitat.RLEnv):
def __init__(self, config: Config, dataset: Optional[Dataset] = None):
self._rl_config = config.RL
self._core_env_config = config.TASK_CONFIG
+ self._reward_measure_name = self._rl_config.REWARD_MEASURE
+ self._success_measure_name = self._rl_config.SUCCESS_MEASURE
- self._previous_target_distance = None
+ self._previous_measure = None
self._previous_action = None
- self._episode_distance_covered = None
- self._success_distance = self._core_env_config.TASK.SUCCESS_DISTANCE
super().__init__(self._core_env_config, dataset)
def reset(self):
self._previous_action = None
-
observations = super().reset()
-
- self._previous_target_distance = self.habitat_env.current_episode.info[
- "geodesic_distance"
+ self._previous_measure = self._env.get_metrics()[
+ self._reward_measure_name
]
return observations
@@ -64,30 +62,18 @@ class NavRLEnv(habitat.RLEnv):
def get_reward(self, observations):
reward = self._rl_config.SLACK_REWARD
- current_target_distance = self._distance_target()
- reward += self._previous_target_distance - current_target_distance
- self._previous_target_distance = current_target_distance
+ current_measure = self._env.get_metrics()[self._reward_measure_name]
+
+ reward += self._previous_measure - current_measure
+ self._previous_measure = current_measure
if self._episode_success():
reward += self._rl_config.SUCCESS_REWARD
return reward
- def _distance_target(self):
- current_position = self._env.sim.get_agent_state().position.tolist()
- distance = self._env.sim.geodesic_distance(
- current_position,
- [goal.position for goal in self._env.current_episode.goals],
- )
- return distance
-
def _episode_success(self):
- if (
- self._env.task.is_stop_called
- and self._distance_target() < self._success_distance
- ):
- return True
- return False
+ return self._env.get_metrics()[self._success_measure_name]
def get_done(self, observations):
done = False
diff --git a/habitat_baselines/config/default.py b/habitat_baselines/config/default.py
index b4a3abbcf789678f14ad9bf1f553f86851716ade..2d3b3f068358acb2e1202ff0cfbcaf7a254d4561 100644
--- a/habitat_baselines/config/default.py
+++ b/habitat_baselines/config/default.py
@@ -47,6 +47,8 @@ _C.EVAL.USE_CKPT_CONFIG = True
# REINFORCEMENT LEARNING (RL) ENVIRONMENT CONFIG
# -----------------------------------------------------------------------------
_C.RL = CN()
+_C.RL.REWARD_MEASURE = "distance_to_goal"
+_C.RL.SUCCESS_MEASURE = "spl"
_C.RL.SUCCESS_REWARD = 10.0
_C.RL.SLACK_REWARD = -0.01
# -----------------------------------------------------------------------------
diff --git a/habitat_baselines/config/objectnav/ddppo_objectnav.yaml b/habitat_baselines/config/objectnav/ddppo_objectnav.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c9d30f2245e0df02425af9cf873865f8390861e2
--- /dev/null
+++ b/habitat_baselines/config/objectnav/ddppo_objectnav.yaml
@@ -0,0 +1,60 @@
+BASE_TASK_CONFIG_PATH: "configs/tasks/objectnav_mp3d.yaml"
+TRAINER_NAME: "ppo"
+ENV_NAME: "NavRLEnv"
+SIMULATOR_GPU_ID: 0
+TORCH_GPU_ID: 1
+VIDEO_OPTION: ["disk", "tensorboard"]
+TENSORBOARD_DIR: "/checkpoint/maksymets/logs/habitat_baselines/ddppo/obj_nav_mp3d"
+VIDEO_DIR: "data/video_dir"
+TEST_EPISODE_COUNT: 2
+EVAL_CKPT_PATH_DIR: "data/new_checkpoints"
+NUM_PROCESSES: 2
+SENSORS: ["DEPTH_SENSOR"]
+CHECKPOINT_FOLDER: "data/new_checkpoints"
+NUM_UPDATES: 10000
+LOG_INTERVAL: 10
+CHECKPOINT_INTERVAL: 50
+
+RL:
+ SUCCESS_REWARD: 2.5
+ PPO:
+ # ppo params
+ clip_param: 0.2
+ ppo_epoch: 2
+ num_mini_batch: 2
+ value_loss_coef: 0.5
+ entropy_coef: 0.01
+ lr: 2.5e-6
+ eps: 1e-5
+ max_grad_norm: 0.2
+ num_steps: 128
+ use_gae: True
+ gamma: 0.99
+ tau: 0.95
+ use_linear_clip_decay: False
+ use_linear_lr_decay: False
+ reward_window_size: 50
+
+ use_normalized_advantage: False
+
+ hidden_size: 512
+
+ DDPPO:
+ sync_frac: 0.6
+ # The PyTorch distributed backend to use
+ distrib_backend: NCCL
+ # Visual encoder backbone
+ pretrained_weights: data/ddppo-models/gibson-2plus-resnet50.pth
+ # Initialize with pretrained weights
+ pretrained: False
+ # Initialize just the visual encoder backbone with pretrained weights
+ pretrained_encoder: False
+ # Whether or not the visual encoder backbone will be trained.
+ train_encoder: True
+ # Whether or not to reset the critic linear layer
+ reset_critic: True
+
+ # Model parameters
+ backbone: resnet50
+ rnn_type: LSTM
+ num_recurrent_layers: 2