logger

log.csv

@@ -0,0 +1,144 @@
+Budapest,Lap Done,1119.0340420000018,135.34
+Budapest,Lap Done,1355.9112149999964,135.35
+Sakhir,collisions,293.22077099999984,27.03
+Monza,Lap Done,1536.9788930000047,146.81
+Silverstone,Lap Done,1590.7086089999987,156.94
+Melbourne,Lap Done,1348.5863180000003,157.47
+Austin,collisions,24.809995000000008,2.68
+Sochi,Lap Done,1609.6109830000028,156.23
+YasMarina,collisions,393.5002970000001,37.04
+Silverstone,Lap Done,1589.3158050000006,156.99
+Nuerburgring,Lap Done,1243.0771860000082,153.91
+Zandvoort,Lap Done,1320.3753559999939,133.61
+Budapest,Lap Done,1356.1212550000046,135.41
+MoscowRaceway,collisions,208.4142869999999,19.05
+Silverstone,Lap Done,1591.8317190000032,156.97
+Sepang,collisions,57.77027999999997,4.19
+Nuerburgring,Lap Done,1532.0329290000045,151.61
+Silverstone,Lap Done,1589.6193800000003,156.89000000000001
+BrandsHatch,Lap Done,1191.6087239999968,116.60000000000001
+MoscowRaceway,collisions,151.8067009999999,14.530000000000001
+Monza,Lap Done,1533.978411999999,146.41
+MexicoCity,collisions,-253.53990500000018,7.1000000000000005
+Spielberg,collisions,9.370373999999998,0.77
+Sochi,collisions,198.82580899999985,15.08
+Melbourne,collisions,217.195128,16.41
+MoscowRaceway,collisions,29.158565999999983,2.22
+Oschersleben,collisions,0.9008170000000001,0.05
+MexicoCity,collisions,-321.27741299999997,3.48
+Sochi,Lap Done,1201.5888190000028,156.25
+YasMarina,collisions,154.06195399999987,13.61
+Budapest,Lap Done,1356.4534230000045,135.33
+Nuerburgring,Lap Done,1539.4956069999967,153.81
+Nuerburgring,Lap Done,1538.6830379999853,153.66
+Monza,collisions,-5.614840999999999,0.06
+Sepang,Lap Done,1687.4734459999959,165.81
+Monza,Lap Done,1536.3372049999987,146.5
+Zandvoort,Lap Done,1318.6443369999995,133.62
+Sochi,Lap Done,1609.3523549999982,156.22
+Oschersleben,Lap Done,521.9784709999992,92.39
+MexicoCity,collisions,91.18013000000006,8.5
+Hockenheim,collisions,604.1101950000051,58.27
+MexicoCity,collisions,562.7361969999997,51.21
+BrandsHatch,Lap Done,1190.0692619999995,116.63
+Nuerburgring,Lap Done,1219.3429289999974,151.61
+Catalunya,Lap Done,1407.5553649999954,139.86
+Catalunya,Lap Done,1410.5743260000067,140.29
+Sochi,Lap Done,1609.5749329999971,156.25
+BrandsHatch,Lap Done,1189.3355509999974,116.58
+Hockenheim,collisions,58.654760999999965,4.2700000000000005
+MoscowRaceway,collisions,9.70535,2.59
+Nuerburgring,Lap Done,1442.4535749999952,115.2
+Spa,collisions,155.31343299999986,11.8
+SaoPaulo,Lap Done,1096.1369300000013,89.60000000000001
+SaoPaulo,Lap Done,1097.1404529999977,89.69
+MoscowRaceway,collisions,-164.75199900000052,22.18
+Sakhir,collisions,667.030277,51.11
+Melbourne,Lap Done,1545.6370629999988,120.47
+Spa,collisions,571.1648049999993,43.49
+MexicoCity,collisions,132.86603799999992,10.33
+SaoPaulo,collisions,54.39952100000001,3.79
+Sepang,collisions,428.7918170000007,38.04
+YasMarina,collisions,394.2968559999997,31.400000000000002
+Silverstone,collisions,537.5302599999999,41.06
+BrandsHatch,Lap Done,1142.1246840000001,89.49
+Budapest,Lap Done,1290.0806910000013,103.21000000000001
+Spielberg,collisions,49.369002999999964,3.35
+Spa,collisions,100.43793700000002,13.27
+BrandsHatch,Lap Done,1143.2661529999973,89.42
+Austin,collisions,66.75350999999999,5.19
+Sepang,collisions,795.293627,60.61
+Catalunya,Lap Done,1338.7730769999998,107.27
+Austin,collisions,31.722481000000005,2.19
+Budapest,collisions,35.444201000000014,2.69
+Zandvoort,Lap Done,1245.7073450000016,95.76
+Spielberg,collisions,300.4100849999997,22.17
+Nuerburgring,collisions,80.27427199999997,5.98
+Nuerburgring,collisions,131.365482,9.46
+Nuerburgring,collisions,232.44411199999993,17.13
+Budapest,collisions,63.988542999999964,4.46
+Sepang,Lap Done,1558.4692349999991,117.07000000000001
+Sepang,Lap Done,1559.5947700000008,117.02
+Budapest,Lap Done,1280.9077359999967,97.31
+Sochi,collisions,302.56653100000005,22.3
+MoscowRaceway,Lap Done,1025.8942210000048,84.33
+MoscowRaceway,Lap Done,523.8762919999991,84.11
+YasMarina,collisions,3.2489410000000003,0.34
+Austin,collisions,192.793103,14.31
+Budapest,Lap Done,1278.535843999999,97.36
+Melbourne,Lap Done,1522.550593000003,112.03
+Hockenheim,collisions,15.118211000000464,29.22
+Silverstone,Lap Done,1476.7647630000054,111.99000000000001
+Monza,collisions,1057.7889119999993,77.62
+Silverstone,Lap Done,1476.7414799999924,112.08
+Austin,collisions,235.3341380000002,18.22
+Sochi,collisions,-98.83033300000024,11.39
+Sochi,collisions,201.05379499999987,16.37
+Nuerburgring,collisions,164.05992199999997,13.31
+Spielberg,collisions,126.34669099999996,9.22
+Zandvoort,collisions,504.4381910000003,40.660000000000004
+Melbourne,collisions,37.310998999998716,33.07
+MexicoCity,collisions,27.00339500000003,2.56
+SaoPaulo,collisions,379.04588200000063,30.45
+Budapest,collisions,678.7042570000028,53.94
+Catalunya,collisions,363.2802249999994,29.22
+Monza,collisions,32.989853000000004,2.17
+MoscowRaceway,collisions,-144.487513,1.51
+Sochi,collisions,108.25450000000005,8.91
+Hockenheim,collisions,106.02874999999993,7.69
+Spielberg,collisions,51.17584999999994,3.87
+Spielberg,collisions,79.29953299999998,5.86
+Budapest,collisions,63.403188,4.68
+Spielberg,collisions,335.27790200000044,26.38
+Budapest,collisions,405.1492030000003,32.32
+MexicoCity,collisions,107.87068699999944,8.44
+Austin,collisions,111.01907099999944,8.36
+Melbourne,collisions,253.10393699999943,19.67
+Austin,collisions,19.739583000000007,1.41
+Zandvoort,collisions,363.30132399999957,44.980000000000004
+Spielberg,collisions,379.7933659999998,26.88
+YasMarina,collisions,89.75034899999991,6.3500000000000005
+Budapest,collisions,443.95240200000035,32.61
+Spielberg,collisions,90.52929099999993,6.3500000000000005
+YasMarina,collisions,-320.927774,3.42
+Sochi,collisions,413.9931290000003,30.61
+Sakhir,collisions,139.1305599999996,9.42
+Budapest,collisions,554.9551910000001,40.58
+Monza,collisions,523.735378999999,36.26
+Monza,collisions,-263.1314959999999,2.7800000000000002
+Spielberg,collisions,428.51690099999934,30.75
+Budapest,collisions,85.43135399999994,6.01
+Spielberg,collisions,268.6757809999995,18.97
+Budapest,collisions,68.48865500000002,4.95
+Sochi,collisions,30.28158,2.14
+MoscowRaceway,collisions,-130.49533200000005,6.04
+Zandvoort,collisions,382.52510400000045,29.5
+BrandsHatch,Lap Done,1132.4173250000015,82.28
+Sepang,collisions,15.132308,1.72
+Catalunya,collisions,454.8668540000004,33.55
+Sepang,Lap Done,1477.6719150000033,165.83
+Sochi,Lap Done,1609.5061790000045,156.21
+Spielberg,Too long,-43273.17756902161,500.01
+Silverstone,Lap Done,1587.9538560000003,156.45000000000002
+Oschersleben,Lap Done,834.3727379999983,92.41
+Spielberg,Too long,-45790.96370202984,500.01

report/index.txt

@@ -0,0 +1,103 @@
+\documentclass[conference]{IEEEtran}
+% \IEEEoverridecommandlockouts
+% The preceding line is only needed to identify funding in the first footnote. If that is unneeded, please comment it out.
+\usepackage{cite}
+\usepackage{amsmath,amssymb,amsfonts}
+\usepackage{algorithmic}
+\usepackage{graphicx}
+\usepackage{textcomp}
+\usepackage{xcolor}
+
+\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
+    T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
+\begin{document}
+
+\title{
+    Autonomous driving model trained in a simulated environment using Reinforcement Learning and operating in a ROS environment
+}
+
+\author{Manuel Andruccioli,
+Tommaso Patriti,
+Giacomo Totaro,\\ 
+\textit{University of Bologna (Italy)} \\
+e-mail: $\{$manuel.andruccioli, tommaso.patriti, giacomo.totato2$\}$@studio.unibo.it }
+
+\maketitle
+
+\begin{abstract}
+Our abstract
+\end{abstract}
+
+\begin{IEEEkeywords}
+    Reinforcement Learning, Deep Learning, Autonomous Racing, ROS
+\end{IEEEkeywords}
+
+\section{Introduction}
+
+Section \cite{test}
+
+\begin{itemize}
+    \item Descrizione del contesto e dell'importanza della guida autonoma nelle macchine.
+
+    \item Presentazione del vostro obiettivo di ricerca e della vostra ipotesi.
+
+\end{itemize}
+
+
+\section{Stato dell'arte}
+
+\begin{itemize}
+    \item Una revisione della letteratura su progetti simili e sull'uso di Reinforcement Learning nelle applicazioni di guida autonoma.
+
+    \item Discussione delle sfide e delle soluzioni proposte da altri ricercatori nel campo.
+
+\end{itemize}
+
+
+\section{Metodologia}
+
+\begin{itemize}
+    \item Descrizione dell'architettura del modello utilizzato, inclusi i dettagli su come avete implementato l'algoritmo PPO.
+
+    \item Spiegazione del processo di raccolta dei dati e la selezione dei circuiti utilizzati per l'addestramento.
+
+    \item Dettagli sui waypoints e su come sono stati integrati nel processo di addestramento.
+
+\end{itemize}
+
+\section{Esperimenti}
+
+\begin{itemize}
+    \item Descrizione delle condizioni sperimentali, tra cui la configurazione dell'addestramento, la scelta dei parametri, ecc.
+
+    \item Presentazione dei risultati ottenuti durante i vostri esperimenti.
+
+    \item Analisi dei risultati, comprese le prestazioni del modello su diversi circuiti.
+
+\end{itemize}
+
+\section{Discussione}
+
+\begin{itemize}
+    \item Interpretazione dei risultati e confronto con la letteratura esistente.
+
+    \item Discussione sulle sfide incontrate e le eventuali limitazioni del vostro approccio.
+
+    \item Possibili sviluppi futuri e miglioramenti proposti.
+
+\end{itemize}
+
+\section{Conclusioni}
+
+\begin{itemize}
+    \item Riassunto dei risultati principali.
+
+    \item Sottolineare l'importanza del vostro contributo e le potenziali implicazioni nella guida autonoma.
+
+\end{itemize}
+
+
+\bibliographystyle{IEEEtran}
+\bibliography{bibliography}
+
+\end{document}

src/train.py

@@ -20,7 +20,7 @@
 
 eval_env  = gym.make('f110_gym:f110-v0', map=map_path, map_ext=map_ext, num_agents=1, timestep=timestep, integrator=Integrator.RK4)
 
-eval_env = F110_Wrapped(eval_env, random_map=False)
+eval_env = F110_Wrapped(eval_env, random_map=True)
 
 eval_env.set_map_path(path)
 

src/wrapper/wrapper.py

@@ -8,6 +8,20 @@
 from pyglet.gl import GL_POINTS
 import utility.map_utility as map_utility
 from typing import Any, Dict, List, Optional, SupportsFloat, Tuple, Union
+import csv
+
+
+def logger(map, event, reword, lap_time):
+    #logger in a csv file
+    map = map.split("/")[-1]
+    with open('log.csv', 'a', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow([map, event, reword, lap_time])
+
+
+
+
+
 
 def convert_range(value, input_range, output_range):
     # converts value(s) from range to another range
@@ -202,6 +216,7 @@ def episode_end(reason = None, rew = 0):
 
             self.count = 0
             if self.one_lap_done:
+                logger(self.map_path, "Lap Done", sum(self.episode_returns), len(self.episode_returns) * 0.01)
                 self.episode_returns = []
                 self.one_lap_done = False
             else:
@@ -218,11 +233,16 @@ def episode_end(reason = None, rew = 0):
 
 
         if observation['collisions'][0]:
+            logger(self.map_path, "collisions", sum(self.episode_returns), len(self.episode_returns) * 0.01)
             done, reward = episode_end(rew = -30)
+
+
 
 
         if len(self.episode_returns) > 50_000:
+            logger(self.map_path, "Too long", sum(self.episode_returns), len(self.episode_returns) * 0.01)
             done, reward = episode_end("Too long", -10)
+
 
 
 

train_test/mean_reward.txt

@@ -1 +1 @@
-1107.9332437999997
+944.3080034500001