Package: rldb
rldbDatabase of RL algorithmsAtari Space Invaders ScoresMuJoCo Walker2d ScoresExamplesYou can userldb.find_all({})to retrieve all existing entries inrldb.importrldball_entries=rldb.find_all({})You can also filter entries by specifying key-value pairs that the entry must match:importrldbdqn_entries=rldb.find_all({'algo-nickname':'DQN'})breakout_noop_entries=rldb.find_all({'env-title':'atari-breakout','env-variant':'No-op start',})You can also userldbl.find_one(filter_dict)to find one entry that matches the key-value pair specified infilter_dict:importrldbimportpprintentry=rldb.find_one({'env-title':'atari-pong','algo-title':'Human',})pprint.pprint(entry)Output{'algo-nickname':'Human','algo-title':'Human','env-title':'atari-pong','env-variant':'No-op start','score':14.6,'source-arxiv-id':'1511.06581','source-arxiv-version':3,'source-authors':['Ziyu Wang','Tom Schaul','Matteo Hessel','Hado van Hasselt','Marc Lanctot','Nando de Freitas'],'source-bibtex':'@article{DBLP:journals/corr/WangFL15,\n''    author    = {Ziyu Wang and\n''                 Nando de Freitas and\n''                 Marc Lanctot},\n''    title     = {Dueling Network Architectures for Deep ''Reinforcement Learning},\n''    journal   ={CoRR},\n''    volume    = {abs/1511.06581},\n''    year      ={2015},\n''    url       = {http://arxiv.org/abs/1511.06581},\n''    archivePrefix ={arXiv},\n''    eprint    ={1511.06581},\n''    timestamp = {Mon, 13 Aug 2018 16:48:17 +0200},\n''    biburl    = ''{https://dblp.org/rec/bib/journals/corr/WangFL15},\n''    bibsource = {dblp computer science bibliography, ''https://dblp.org}\n''}','source-nickname':'DuDQN','source-title':'Dueling Network Architectures for Deep Reinforcement ''Learning'}Entry StructureHere is the format of every entry:{# BASICS"source-title":"","source-nickname":"","source-authors":[],# MISC."source-bibtex":"",# ALGORITHM"algo-title":"","algo-nickname":"","algo-source-title":"",# SCORE"env-title":"","score":0,}source-titleis the full title of the source of the score: it can be the title of the paper or GitHub repository title.source-nicknameis a popular nickname or acronym for that title if it exists, otherwise it is the same assource-title.source-authorsare a list of authors or contributors.source-bibtexis a BibTeX-format citation.algo-titleis the full title of the algorithm used.algo-nicknameis the nickname or acronym for that algorithm if it exists, otherwise it is the same asalgo-nickname.algo-source-titleis the title of the source of thealgorithm. It can and often is different fromsource-title.For example, theSpace Invadersscore ofAsynchronous Advantage Actor Critic (A3C)algorithm in theNoisy Networks for Exploration (NoisyNet)paper is represented by the following entry:{#  BASICS"source-title":"Noisy Networks for Exploration","source-nickname":"NoisyNet","source-authors":["Meire Fortunato","Mohammad Gheshlaghi Azar","Bilal Piot","Jacob Menick","Ian Osband","Alex Graves","Vlad Mnih","Remi Munos","Demis Hassabis","Olivier Pietquin","Charles Blundell","Shane Legg",],#  ARXIV"source-arxiv-id":"1706.10295","source-arxiv-version":2,#  MISC."source-bibtex":"""@article{DBLP:journals/corr/FortunatoAPMOGM17,author    = {Meire Fortunato andMohammad Gheshlaghi Azar andBilal Piot andJacob Menick andIan Osband andAlex Graves andVlad Mnih andR{\'{e}}mi Munos andDemis Hassabis andOlivier Pietquin andCharles Blundell andShane Legg},title     = {Noisy Networks for Exploration},journal   ={CoRR},volume    = {abs/1706.10295},year      ={2017},url       = {http://arxiv.org/abs/1706.10295},archivePrefix ={arXiv},eprint    ={1706.10295},timestamp = {Mon, 13 Aug 2018 16:46:11 +0200},biburl    = {https://dblp.org/rec/bib/journals/corr/FortunatoAPMOGM17},bibsource = {dblp computer science bibliography, https://dblp.org}}""",# ALGORITHM"algo-title":"Asynchronous Advantage Actor Critic","algo-nickname":"A3C","algo-source-title":"Asynchronous Methods for Deep Reinforcement Learning",# HYPERPARAMETERS"algo-frames":320*1000*1000,# Number of frames# SCORE"env-title":"atari-space-invaders","env-variant":"No-op start","score":1034,"stddev":49,}Note that, as shown here, the entry can contain additional information.SourcesPapersDeep Q-NetworksPlaying Atari with Deep Reinforcement Learning (Mnih et al., 2013)Human-level control through deep reinforcement learning (Mnih et al., 2015)Deep Recurrent Q-Learning for Partially Observable MDPs (Hausknecht and Stone, 2015)Massively Parallel Methods for Deep Reinforcement Learning (Nair et al., 2015)Deep Reinforcement Learning with Double Q-learning (Hasselt et al., 2015)Prioritized Experience Replay (Schaul et al., 2015)Dueling Network Architectures for Deep Reinforcement Learning (Wang et al., 2015)Noisy Networks for Exploration (Fortunato et al., 2017)A Distributional Perspective on Reinforcement Learning (Bellemare et al., 2017)Rainbow: Combining Improvements in Deep Reinforcement Learning (Hessel et al., 2017)Distributional Reinforcement Learning with Quantile Regression (Dabney et al., 2017)Implicit Quantile Networks for Distributional Reinforcement Learning (Dabney et al., 2018)Policy GradientsAsynchronous Methods for Deep Reinforcement Learning (Mnih et al., 2016)Trust Region Policy Optimization (Schulman et al., 2015)Proximal Policy Optimization Algorithms (Schulman et al., 2017)Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (Wu et al., 2017)Addressing Function Approximation Error in Actor-Critic Methods (Fujimoto et al., 2018)IMPALA: Importance Weighted Actor-Learner Architectures (Espeholt et al., 2018)The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning (Gruslys et al., 2017)ExplorationExploration by Random Network Distillation (Burda et al., 2018)Misc.Trust-PCL: An Off-Policy Trust Region Method for Continuous Control (Nachum et al., 2017)RepositoriesOpenAI BaselinesRL Baselines Zoo