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akashvelu
requested review from
AboudyKreidieh,
cathywu,
eugenevinitsky and
kanaadp
as code owners
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| <?xml version="1.0" encoding="UTF-8"?> | ||
| <module type="PYTHON_MODULE" version="4"> | ||
| <component name="NewModuleRootManager"> | ||
| <content url="file://$MODULE_DIR$" /> | ||
| <orderEntry type="jdk" jdkName="Python 3.6 (flow)" jdkType="Python SDK" /> | ||
| <orderEntry type="sourceFolder" forTests="false" /> | ||
| </component> | ||
| <component name="PyDocumentationSettings"> | ||
| <option name="format" value="PLAIN" /> | ||
| <option name="myDocStringFormat" value="Plain" /> | ||
| </component> | ||
| </module> No newline at end of file |
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Nit: please remove this file.
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, |
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| """Multi-agent I-210 example. | ||
| Trains a non-constant number of agents, all sharing the same policy, on the | ||
| highway with ramps network. | ||
| """ | ||
| import os | ||
| import numpy as np | ||
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| from ray.tune.registry import register_env |
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This file seems identical to existing code?
| """ | ||
| # Implementation in Tensorflow | ||
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| def __init__(self, veh_id, action_network, multiagent, car_following_params=None, time_delay=0.0, noise=0, fail_safe=None): |
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Please add a docstring so we can know what action_network is.
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| with tf.variable_scope(policy_scope, reuse=tf.AUTO_REUSE): | ||
| self.build_network() |
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Why do you need an AUTO_REUSE here?
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I put an AUTO_REUSE here so that the same variables will be reused when the graph is rerun (so copies of the variables (weights/biases) don't get recreated)
| self.action_predictions = pred_action | ||
| print("TYPE: ", type(self.obs_placeholder)) | ||
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| if self.inject_noise == 1: |
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Nit: conventionally you don't need to check a bool like this
| Defines input, output, and training placeholders for neural net | ||
| """ | ||
| self.obs_placeholder = tf.placeholder(shape=[None, self.obs_dim], name="obs", dtype=tf.float32) | ||
| self.action_placeholder = tf.placeholder(shape=[None, self.action_dim], name="action", dtype=tf.float32) |
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So for stochastic algorithms, they are parametrized by a mean and standard deviation of a gaussian that you sample from. It'd be cool to add this as an option here so we can use PPO
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This current implementation can be used for deterministic algorithms like DDPG and TD3 which is great
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| if len(observation.shape)<=1: | ||
| observation = observation[None] |
| # network expects an array of arrays (matrix); if single observation (no batch), convert to array of arrays | ||
| if len(observation.shape)<=1: | ||
| observation = observation[None] | ||
| ret_val = self.sess.run([self.action_predictions], feed_dict={self.obs_placeholder: observation})[0] |
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You should make it clear here that this is returning 1 accel and will not operate correctly if you pass a batch
* deleting unworking params from SumoChangeLaneParams * deleted unworking params, sublane working in highway : * moved imports inside functions * Apply suggestions from code review * bug fixes * bug fix Co-authored-by: Aboudy Kreidieh <akreidieh@gmail.com>
* added function to kernel/vehicle to get number of not departed vehiles * fixed over indentation of the docstring * indentation edit * pep8 Co-authored-by: AboudyKreidieh <akreidieh@gmail.com>
* changed _departed_ids, and _arrived_ids in the update function * fixed bug in get_departed_ids and get_arrived_ids
Time-Space Diagram greyed regions
Add accel penalty, stop penalty, mpg reward, and ability to compute reward for any vehicles upstream of you (i.e. make you less greedy and more social)
* New energy class to inventory multiple energy models Co-authored-by: Joy Carpio <engrjoycarpio@berkeley.edu>
* Add time-space diagram plotting to experiment.py
* prereq dict added to query * prereq checking mechanism implemented, not tested yet * prereq checking tested * change to more flexible filter handling * make safety_rate and safety_max_value floats * ignore nulls in fact_top_scores * fix typo * remove unneeded import * replace uneccessary use of list to set * add queries to pre-bin histogram data * fix the serialization issue with set, convert to list before write as json * fix query * fix query * fixed query bug Co-authored-by: liljonnystyle <jonny5@berkeley.edu>
* update tacoma power demand query, meters/Joules -> mpg conversion
* fix some implementation errors in energy models * pull i210_dev and fix flake8
Add --multi_node flag
* implement HighwayNetwork for Time-Space Diagrams (#979) * fixed h-baselines bug (#982) * Replicated changes in 867. Done bug (#980) * Aimsun changes minus reset * removed crash attribute * tensorflow 1.15.2 * merge custom output and failsafes to master (#981) * add write_to_csv() function to master * include pipeline README.md * add data pipeline __init__ * add experiment.py changes * add write_to_csv() function to master * change warning print to ValueError message * update to new update_accel methods * add display_warnings boolean * add get_next_speed() function to base vehicle class * revert addition of get_next_speed * merge custom output and failsafes to master * add write_to_csv() function to master * add display_warnings boolean * add get_next_speed() function to base vehicle class * revert addition of get_next_speed * revert change to get_feasible_action call signature * change print syntax to be python3.5 compliant * add tests for new failsafe features * smooth default to True * rearrange raise exception for test coverage * moved simulation logging to the simulation kernel (#991) * add 210 edgestarts for backwards compatibility (#985) * fastforward PR 989 * fix typo * Requirements update (#963) * updated requirements.txt and environment.yml * Visualizer tests fixes * remove .func * move all miles_per_* rewards to instantaneous_mpg * update reward fns to new get_accel() method * made tests faster * some fixes to utils * change the column order, modify the pipeline to use SUMO emission file * write metadata to csv * change apply_acceleration smoothness setting * make save_csv return the file paths Co-authored-by: AboudyKreidieh <akreidieh@gmail.com> Co-authored-by: liljonnystyle <jonny5@berkeley.edu> Co-authored-by: Kathy Jang <kathyjang@gmail.com> Co-authored-by: Nathan Lichtlé <nathanlct@icloud.com> Co-authored-by: akashvelu <akashvelu@berkeley.edu> Co-authored-by: Brent Zhao <brentgryffindor@outlook.com>
* refactor tsd to allow for axes offsets * update time-space plotter unit tests
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Pull request information
Description
Adds functionality to do imitation learning (with DAgger), to train a model to imitate an expert.