kengz / SLM-Lab

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
https://slm-lab.gitbook.io/slm-lab/
MIT License
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Full benchmark upload #427

Closed kengz closed 4 years ago

kengz commented 4 years ago

Full benchmark upload

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Discrete Benchmark

Env. \ Alg. DQN DDQN+PER A2C (GAE) A2C (n-step) PPO SAC
Breakout
graph
80.88 182 377 398 443 -
Pong
graph
18.48 20.5 19.31 19.56 20.58 19.87*
Seaquest
graph
1185 4405 1070 1684 1715 -
Qbert
graph
5494 11426 12405 13590 13460 214*
LunarLander
graph
192 233 25.21 68.23 214 276
UnityHallway
graph
-0.32 0.27 0.08 -0.96 0.73 -
UnityPushBlock
graph
4.88 4.93 4.68 4.93 4.97 -

Episode score at the end of training attained by SLM Lab implementations on discrete-action control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. Results marked with * were trained using the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time.

For the full Atari benchmark, see Atari Benchmark

Continuous Benchmark

Env. \ Alg. A2C (GAE) A2C (n-step) PPO SAC
RoboschoolAnt
graph
787 1396 1843 2915
RoboschoolAtlasForwardWalk
graph
59.87 88.04 172 800
RoboschoolHalfCheetah
graph
712 439 1960 2497
RoboschoolHopper
graph
710 285 2042 2045
RoboschoolInvertedDoublePendulum
graph
996 4410 8076 8085
RoboschoolInvertedPendulum
graph
995 978 986 941
RoboschoolReacher
graph
12.9 10.16 19.51 19.99
RoboschoolWalker2d
graph
280 220 1660 1894
RoboschoolHumanoid
graph
99.31 54.58 2388 **2621***
RoboschoolHumanoidFlagrun
graph
73.57 178 2014 **2056***
RoboschoolHumanoidFlagrunHarder
graph
-429 253 680 280*
Unity3DBall
graph
33.48 53.46 78.24 98.44
Unity3DBallHard
graph
62.92 71.92 91.41 97.06

Episode score at the end of training attained by SLM Lab implementations on continuous control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. Results marked with * require 50M-100M frames, so we use the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time.

Atari Benchmark

Env. \ Alg. DQN DDQN+PER A2C (GAE) A2C (n-step) PPO
Adventure
graph
-0.94 -0.92 -0.77 -0.85 -0.3
AirRaid
graph
1876 3974 4202 3557 4028
Alien
graph
822 1574 1519 1627 1413
Amidar
graph
90.95 431 577 418 795
Assault
graph
1392 2567 3366 3312 3619
Asterix
graph
1253 6866 5559 5223 6132
Asteroids
graph
439 426 2951 2147 2186
Atlantis
graph
68679 644810 2747371 2259733 2148077
BankHeist
graph
131 623 855 1170 1183
BattleZone
graph
6564 6395 4336 4533 13649
BeamRider
graph
2799 5870 2659 4139 4299
Berzerk
graph
319 401 1073 763 860
Bowling
graph
30.29 39.5 24.51 23.75 31.64
Boxing
graph
72.11 90.98 1.57 1.26 96.53
Breakout
graph
80.88 182 377 398 443
Carnival
graph
4280 4773 2473 1827 4566
Centipede
graph
1899 2153 3909 4202 5003
ChopperCommand
graph
1083 4020 3043 1280 3357
CrazyClimber
graph
46984 88814 106256 109998 116820
Defender
graph
281999 313018 665609 657823 534639
DemonAttack
graph
1705 19856 23779 19615 121172
DoubleDunk
graph
-21.44 -22.38 -5.15 -13.3 -6.01
ElevatorAction
graph
32.62 17.91 9966 8818 6471
Enduro
graph
437 959 787 0.0 1926
FishingDerby
graph
-88.14 -1.7 16.54 1.65 36.03
Freeway
graph
24.46 30.49 30.97 0.0 32.11
Frostbite
graph
98.8 2497 277 261 1062
Gopher
graph
1095 7562 929 1545 2933
Gravitar
graph
87.34 258 313 433 223
Hero
graph
1051 12579 16502 19322 17412
IceHockey
graph
-14.96 -14.24 -5.79 -6.06 -6.43
Jamesbond
graph
44.87 702 521 453 561
JourneyEscape
graph
-4818 -2003 -921 -2032 -1094
Kangaroo
graph
1965 8897 67.62 554 4989
Krull
graph
5522 6650 7785 6642 8477
KungFuMaster
graph
2288 16547 31199 25554 34523
MontezumaRevenge
graph
0.0 0.02 0.08 0.19 1.08
MsPacman
graph
1175 2215 1965 2158 2350
NameThisGame
graph
3915 4474 5178 5795 6386
Phoenix
graph
2909 8179 16345 13586 30504
Pitfall
graph
-68.83 -73.65 -101 -31.13 -35.93
Pong
graph
18.48 20.5 19.31 19.56 20.58
Pooyan
graph
1958 2741 2862 2531 6799
PrivateEye
graph
784 303 93.22 78.07 50.12
Qbert
graph
5494 11426 12405 13590 13460
Riverraid
graph
953 10492 8308 7565 9636
RoadRunner
graph
15237 29047 30152 31030 32956
Robotank
graph
3.43 9.05 2.98 2.27 2.27
Seaquest
graph
1185 4405 1070 1684 1715
Skiing
graph
-14094 -12883 -19481 -14234 -24713
Solaris
graph
612 1396 2115 2236 1892
SpaceInvaders
graph
451 670 733 750 797
StarGunner
graph
3565 38238 44816 48410 60579
Tennis
graph
-23.78 -10.33 -22.42 -19.06 -11.52
TimePilot
graph
2819 1884 3331 3440 4398
Tutankham
graph
35.03 159 161 175 211
UpNDown
graph
2043 11632 89769 18878 262208
Venture
graph
4.56 9.61 0.0 0.0 11.84
VideoPinball
graph
8056 79730 35371 40423 58096
WizardOfWor
graph
869 328 1516 1247 4283
YarsRevenge
graph
5816 15698 27097 11742 10114
Zaxxon
graph
442 54.28 64.72 24.7 641

The table above presents results for 62 Atari games. All agents were trained for 10M frames (40M including skipped frames). Reported results are the episode score at the end of training, averaged over the previous 100 evaluation checkpoints with each checkpoint averaged over 4 Sessions. Agents were checkpointed every 10k training frames.