From 2c91e4868ca9437e5e75450e4df784feb9310866 Mon Sep 17 00:00:00 2001
From: "S.P. Mohanty" <spmohanty91@gmail.com>
Date: Fri, 18 Oct 2019 19:04:51 +0200
Subject: [PATCH] Add a custom observation builder
---
my_observation_builder.py | 106 ++++++++++++++++++++++++++++++++++++++
run.py | 29 +++++++----
2 files changed, 125 insertions(+), 10 deletions(-)
create mode 100644 my_observation_builder.py
diff --git a/my_observation_builder.py b/my_observation_builder.py
new file mode 100644
index 0000000..da2a209
--- /dev/null
+++ b/my_observation_builder.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+
+import collections
+from typing import Optional, List, Dict, Tuple
+
+import numpy as np
+
+from flatland.core.env import Environment
+from flatland.core.env_observation_builder import ObservationBuilder
+from flatland.core.env_prediction_builder import PredictionBuilder
+from flatland.core.grid.grid4_utils import get_new_position
+from flatland.core.grid.grid_utils import coordinate_to_position
+from flatland.envs.agent_utils import RailAgentStatus, EnvAgent
+from flatland.utils.ordered_set import OrderedSet
+
+
+class CustomObservationBuilder(ObservationBuilder):
+ """
+ Template for building a custom observation builder for the RailEnv class
+
+ The observation in this case composed of the following elements:
+
+ - transition map array with dimensions (env.height, env.width),\
+ where the value at X,Y will represent the 16 bits encoding of transition-map at that point.
+
+ - the individual agent object (with position, direction, target information available)
+
+ """
+ def __init__(self):
+ super(CustomObservationBuilder, self).__init__()
+
+ def set_env(self, env: Environment):
+ super().set_env(env)
+ # Note :
+ # The instantiations which depend on parameters of the Env object should be
+ # done here, as it is only here that the updated self.env instance is available
+ self.rail_obs = np.zeros((self.env.height, self.env.width))
+ print("Env Width : ", self.env.width, "Env Height : ", self.env.height)
+
+ def reset(self):
+ """
+ Called internally on every env.reset() call,
+ to reset any observation specific variables that are being used
+ """
+ self.rail_obs[:] = 0
+ for _x in range(self.env.width):
+ for _y in range(self.env.height):
+ # Get the transition map value at location _x, _y
+ transition_value = self.env.rail.get_full_transitions(_y, _x)
+ self.rail_obs[_y, _x] = transition_value
+ print("Responding to obs_builder.reset()")
+
+ def get(self, handle: int = 0):
+ """
+ Returns the built observation for a single agent with handle : handle
+
+ In this particular case, we return
+ - the global transition_map of the RailEnv,
+ - a tuple containing, the current agent's:
+ - state
+ - position
+ - direction
+ - initial_position
+ - target
+ """
+
+ agent = self.env.agents[handle]
+ """
+ Available information for each agent object :
+
+ - agent.status : [RailAgentStatus.READY_TO_DEPART, RailAgentStatus.ACTIVE, RailAgentStatus.DONE]
+ - agent.position : Current position of the agent
+ - agent.direction : Current direction of the agent
+ - agent.initial_position : Initial Position of the agent
+ - agent.target : Target position of the agent
+ """
+
+ status = agent.status
+ position = agent.position
+ direction = agent.direction
+ initial_position = agent.initial_position
+ target = agent.target
+
+
+ """
+ You can also optionally access the states of the rest of the agents by
+ using something similar to
+
+ for i in range(len(self.env.agents)):
+ other_agent: EnvAgent = self.env.agents[i]
+
+ # ignore other agents not in the grid any more
+ if other_agent.status == RailAgentStatus.DONE_REMOVED:
+ continue
+
+ ## Gather other agent specific params
+ other_agent_status = other_agent.status
+ other_agent_position = other_agent.position
+ other_agent_direction = other_agent.direction
+ other_agent_initial_position = other_agent.initial_position
+ other_agent_target = other_agent.target
+
+ ## Do something nice here if you wish
+ """
+ return self.rail_obs, (status, position, direction, initial_position, target)
+
diff --git a/run.py b/run.py
index 15a97b8..5c5bb9a 100644
--- a/run.py
+++ b/run.py
@@ -1,6 +1,6 @@
from flatland.evaluators.client import FlatlandRemoteClient
-from flatland.envs.observations import TreeObsForRailEnv
-from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+from flatland.core.env_observation_builder import DummyObservationBuilder
+from my_observation_builder import CustomObservationBuilder
import numpy as np
import time
@@ -31,10 +31,14 @@ def my_controller(obs, number_of_agents):
# the example here :
# https://gitlab.aicrowd.com/flatland/flatland/blob/master/flatland/envs/observations.py#L14
#####################################################################
-my_observation_builder = TreeObsForRailEnv(
- max_depth=3,
- predictor=ShortestPathPredictorForRailEnv()
- )
+my_observation_builder = CustomObservationBuilder()
+
+# Or if you want to use your own approach to build the observation from the env_step,
+# please feel free to pass a DummyObservationBuilder() object as mentioned below,
+# and that will just return a placeholder True for all observation, and you
+# can build your own Observation for all the agents as your please.
+# my_observation_builder = DummyObservationBuilder()
+
#####################################################################
# Main evaluation loop
@@ -55,9 +59,11 @@ while True:
# You can also pass your custom observation_builder object
# to allow you to have as much control as you wish
# over the observation of your choice.
+ time_start = time.time()
observation, info = remote_client.env_create(
obs_builder_object=my_observation_builder
)
+ env_creation_time = time.time() - time_start
if not observation:
#
# If the remote_client returns False on a `env_create` call,
@@ -66,7 +72,7 @@ while True:
# and hence its safe to break out of the main evaluation loop
break
- #print("Evaluation Number : {}".format(evaluation_number))
+ print("Evaluation Number : {}".format(evaluation_number))
#####################################################################
# Access to a local copy of the environment
@@ -95,12 +101,12 @@ while True:
# or when the number of time steps has exceed max_time_steps, which
# is defined by :
#
- # max_time_steps = int(1.5 * (env.width + env.height))
+ # max_time_steps = int(4 * 2 * (env.width + env.height + 20))
#
time_taken_by_controller = []
time_taken_per_step = []
-
- for k in range(10):
+ steps = 0
+ while True:
#####################################################################
# Evaluation of a single episode
#
@@ -119,6 +125,7 @@ while True:
# are returned by the remote copy of the env
time_start = time.time()
observation, all_rewards, done, info = remote_client.env_step(action)
+ steps += 1
time_taken = time.time() - time_start
time_taken_per_step.append(time_taken)
@@ -136,6 +143,8 @@ while True:
print("="*100)
print("Evaluation Number : ", evaluation_number)
print("Current Env Path : ", remote_client.current_env_path)
+ print("Env Creation Time : ", env_creation_time)
+ print("Number of Steps : ", steps)
print("Mean/Std of Time taken by Controller : ", np_time_taken_by_controller.mean(), np_time_taken_by_controller.std())
print("Mean/Std of Time per Step : ", np_time_taken_per_step.mean(), np_time_taken_per_step.std())
print("="*100)
--
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