From bcd064590a17c04e0549832c22d5bb80f8c2d8aa Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Tue, 9 Jul 2019 07:51:17 -0400
Subject: [PATCH] minor bugfixes. Simple distance map test (thanks Christian).
This test will be enhanced soon.
---
flatland/envs/observations.py | 6 ++++--
flatland/envs/rail_env.py | 34 +++++++++++++++++++++++-----------
2 files changed, 27 insertions(+), 13 deletions(-)
diff --git a/flatland/envs/observations.py b/flatland/envs/observations.py
index 18af8a0c..594c6d1f 100644
--- a/flatland/envs/observations.py
+++ b/flatland/envs/observations.py
@@ -16,8 +16,10 @@ class TreeObsForRailEnv(ObservationBuilder):
TreeObsForRailEnv object.
This object returns observation vectors for agents in the RailEnv environment.
- The information is local to each agent and exploits the tree structure of the rail
+ The information is local to each agent and exploits the graph structure of the rail
network to simplify the representation of the state of the environment for each agent.
+
+ For details about the features in the tree observation see the get() function.
"""
observation_dim = 9
@@ -204,7 +206,7 @@ class TreeObsForRailEnv(ObservationBuilder):
[... from 'right] +
[... from 'back']
- Finally, each node information is composed of 8 floating point values:
+ Each node information is composed of 9 features:
#1: if own target lies on the explored branch the current distance from the agent in number of cells is stored.
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 8abfd1b3..85043119 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -1,8 +1,5 @@
"""
-Definition of the RailEnv environment and related level-generation functions.
-
-Generator functions are functions that take width, height and num_resets as arguments and return
-a GridTransitionMap object.
+Definition of the RailEnv environment.
"""
# TODO: _ this is a global method --> utils or remove later
@@ -46,20 +43,35 @@ class RailEnv(Environment):
to avoid bottlenecks.
The valid actions in the environment are:
- 0: do nothing
- 1: turn left and move to the next cell; if the agent was not moving, movement is started
- 2: move to the next cell in front of the agent; if the agent was not moving, movement is started
- 3: turn right and move to the next cell; if the agent was not moving, movement is started
- 4: stop moving
+
+ - 0: do nothing (continue moving or stay still)
+ - 1: turn left at switch and move to the next cell; if the agent was not moving, movement is started
+ - 2: move to the next cell in front of the agent; if the agent was not moving, movement is started
+ - 3: turn right at switch and move to the next cell; if the agent was not moving, movement is started
+ - 4: stop moving
Moving forward in a dead-end cell makes the agent turn 180 degrees and step
to the cell it came from.
+
The actions of the agents are executed in order of their handle to prevent
deadlocks and to allow them to learn relative priorities.
- TODO: WRITE ABOUT THE REWARD FUNCTION, and possibly allow for alpha and
- beta to be passed as parameters to __init__().
+ Reward Function:
+
+ It costs each agent a step_penalty for every time-step taken in the environment. Independent of the movement
+ of the agent. Currently all other penalties such as penalty for stopping, starting and invalid actions are set to 0.
+
+ alpha = 1
+ beta = 1
+ Reward function parameters:
+
+ - invalid_action_penalty = 0
+ - step_penalty = -alpha
+ - global_reward = beta
+ - stop_penalty = 0 # penalty for stopping a moving agent
+ - start_penalty = 0 # penalty for starting a stopped agent
+
"""
def __init__(self,
--
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