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timurgepard edited this page Apr 30, 2024 · 494 revisions

This page tracks the performance of user algorithms for various tasks in gym. Previously, users could submit their scores directly to gym.openai.com/envs, but it has been decided that a simpler wiki might do this task more efficiently.

This wiki page is a community driven page. Anyone can edit this page and add to it. We encourage you to contribute and modify this page and add your scores and links to your write-ups and code to reproduce your results. We also encourage you to add new tasks with the gym interface, but not in the core gym library (such as roboschool) to this page as well.

Links to videos are optional, but encouraged. Videos can be youtube, instagram, a tweet, or other public links. Write-ups should explain how to reproduce the result, and can be in the form of a simple gist link, blog post, or github repo.

We have begun to copy over the previous performance scores and write-up links over from the previous page. This is an ongoing effort, and we can use some help.

Environments

Classic control

CartPole-v0

A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center.
  • Environment Details

  • CartPole-v0 defines "solving" as getting average reward of 195.0 over 100 consecutive trials.

  • This environment corresponds to the version of the cart-pole problem described by Barto, Sutton, and Anderson [Barto83].

User Episodes before solve Write-up Video
Zhiqing Xiao 0 (use close-form preset policy) writeup
Henry Jia 0 (use close-form PID policy) code/writeup
Keavnn 0 writeup
Shakti Kumar 0 writeup Video
Mathias Åsberg 🔥 0 writeup Video
[Adrien Kegreisz] (https://github.com/Adrienkgz) 0 ( pre-trained q-network / Infinite Cartpole ) writeup
iRyanBell 2 writeup
Adam 3 (36) writeup
Daniel Sallander 4 writeup
Kapil Chauhan 4 writeup
Ritika Kapoor 7 (use genetic algorithm) writeup
Ben Harris 12 writeup video
Tiger37 14 (0) writeup video
Blake Richey 20 writeup
LukaszFuszara 22 writeup video
MisterTea, econti 24 writeup
Roald Brønstad 24 writeup
yingzwang 32 writeup
sharvar 33 writeup
nuggfr 38 writeup
SurenderHarsha 40 writeup
Chrispresso 45 writeup
n1try 85 writeup
khev 96 writeup video
ceteke 99 writeup
manikanta 100 writeup video
BS Haney 100 Write-up YouTube
Trevor McInroe 130 writeup
JamesUnicomb 145 writeup video
Nihal T Rao 184 writeup video
Harshit Singh Lodha 265 writeup gif
XYTriste 286 writeup
mbalunovic 306 writeup
onimaru 355 writeup video
Google Search "M Kunthe" 382 writeup

MountainCar-v0

A car is on a one-dimensional track, positioned between two "mountains". The goal is to drive up the mountain on the right; however, the car's engine is not strong enough to scale the mountain in a single pass. Therefore, the only way to succeed is to drive back and forth to build up momentum.
  • Environment details
  • MountainCar-v0 defines "solving" as getting average reward of -110.0 over 100 consecutive trials.
  • This problem was first described by Andrew Moore in his PhD thesis [Moore90].
User Episodes before solve Write-up Video
Zhiqing Xiao 0 (use close-form preset policy) writeup
Leocus 10 (1150) writeup
Keavnn 47 writeup
Zhiqing Xiao 75 writeup video
Mohith Sakthivel 90 writeup
Tiger37 224 writeup video
Anas Mohamed 341 Link Link
Harshit Singh Lodha 643 writeup gif
Colin M 944 writeup gif
jing582 1119
DaveLeongSingapore 1967
Pechckin 30 writeup
Amit 1000-1200 writeup video
Gleb I 100 writeup

MountainCarContinuous-v0

A car is on a one-dimensional track, positioned between two "mountains". The goal is to drive up the mountain on the right; however, the car's engine is not strong enough to scale the mountain in a single pass. Therefore, the only way to succeed is to drive back and forth to build up momentum. Here, the reward is greater if you spend less energy to reach the goal

Here, this is the continuous version.

  • Environment details
  • MountainCarContinuous-v0 defines "solving" as getting average reward of 90.0 over 100 consecutive trials.
  • This problem was first described by Andrew Moore in his PhD thesis [Moore90].
User Episodes before solve Write-up Video
Zhiqing Xiao 0 (use close-form preset policy) writeup
Ashioto 1 writeup
timurgepard 5 (Symphony🎹 ver 2.0) writeup video
Mathias Åsberg 🤖 9 writeup Video
Keavnn 11 writeup
camigord 18 writeup
Tobias Steidle 32 writeup video
lirnli 33 writeup
khev 130 writeup video
Sanket Thakur 140 writeup video
Pechckin 1 writeup
Nikhil Barhate 200 (HAC) writeup gif

Pendulum-v0

The inverted pendulum swingup problem is a classic problem in the control literature. In this version of the problem, the pendulum starts in a random position, and the goal is to swing it up so it stays upright.
  • Environment details
  • Pendulum-v0 is an unsolved environment, which means it does not have a specified reward threshold at which it's considered solved.
User Best 100-episode performance Write-up Video
KanishkNavale -106.9528 MultiAgent Policy
msinto93 -123.11 ± 6.86 D4PG
msinto93 -123.79 ± 6.90 DDPG
heerad -134.48 ± 9.07 writeup
BS Haney -135 Write-up YouTube
ThyrixYang -136.16 ± 11.97 writeup
MaelFrancesc -146.4 (mean 900 ep) writeup
lirnli -152.24 ± 10.87 writeup

Acrobot-v1

The acrobot system includes two joints and two links, where the joint between the two links is actuated. Initially, the links are hanging downwards, and the goal is to swing the end of the lower link up to a given height.

  • Acrobot-v1 is an unsolved environment, which means it does not have a specified reward threshold at which it's considered solved.*
  • Control of Acrobot around equilibrium was described by J. Hauser and R. Murray in ACC 1990. Swing-up control of Acrobot is in M. W. Spong, IEEE Control Systems Magazine, 1995.
  • Learning control on Acrobot was first described by Sutton [Sutton96]. We are using the version from RLPy [Geramiford15], which uses Runge-Kutta integration for better accuracy.
User Best 100-episode performance Write-up Video
mallochio -42.37 ± 4.83 taken down
marunowskia -59.31 ± 1.23
MontrealAI -60.82 ± 0.06
BS Haney -61.8 Write-up YouTube
Felix Nica -63.13 ± 2.65 Write-up YouTube
Nick Kaparinos -64.30 ± 4.10 Write-up gif
Daniel Barbosa -67.18 writeup
Mahmood Khordoo -68.63 writeup gif
lirnli -72.09 ± 1.15
Tiger37 -74.49 ± 10.87 writeup
tsdaemon -77.87 ± 1.54
a7b23 -80.68 ± 1.18
Tzoof Avny Brosh -80.73 writeup
DaveLeongSingapore -84.02 ± 1.46
Sanket Thakur -89.29 writeup video
loicmarie -99.18 ± 2.60
simonoso -113.66 ± 5.15
alebac -427.26 ± 15.02
mehdimerai -500.00 ± 0.00

Box2D

LunarLander-v2

Landing pad is always at coordinates (0,0). Coordinates are the first two numbers in state vector. Reward for moving from the top of the screen to landing pad and zero speed is about 100..140 points. If lander moves away from landing pad it loses reward back. Episode finishes if the lander crashes or comes to rest, receiving additional -100 or +100 points. Each leg ground contact is +10. Firing main engine is -0.3 points each frame. Solved is 200 points. Landing outside landing pad is possible. Fuel is infinite, so an agent can learn to fly and then land on its first attempt. Four discrete actions available: do nothing, fire left orientation engine, fire main engine, fire right orientation engine.
  • LunarLander-v2 defines "solving" as getting average reward of 200 over 100 consecutive trials.=
  • by @olegklimov
User Episodes before solve Write-up Video
Keavnn 16 writeup
liu 29 (Average:100) Write-up
Ash Bellett 101 Write-up Video
Mathias Åsberg 🔥 133 writeup Video
Aman Arora 141 Write-up [Under progress]
A. Myachin and R. Potemin 231 Write-up GIF
Daniel T. Plop 295 Write-up GIF
Nick Kaparinos 420 Write-up gif
Sanket Thakur 454 Write-up Video
Mahmood Khordoo 602 Writup gif
Christoph Powazny 658 writeup gif
Daniel Barbosa 674 writeup gif
Xinli Yu 805 writeup gif
Ruslan Miftakhov 814 writeup gif
Ollie Graham 987 writeup gif
Leocus 1000 (21000) writeup
Nikhil Barhate 1500 writeup gif
Udacity DRLND Team 1504 writeup gif
Sigve Rokenes 1590 writeup gif
JamesUnicomb 2100 writeup video
ksankar 2148 Working on it
koltafrickenfer 499474 writeup youtube

LunarLanderContinuous-v2

Landing pad is always at coordinates (0,0). Coordinates are the first two numbers in state vector. Reward for moving from the top of the screen to landing pad and zero speed is about 100..140 points. If lander moves away from landing pad it loses reward back. Episode finishes if the lander crashes or comes to rest, receiving additional -100 or +100 points. Each leg ground contact is +10. Firing main engine is -0.3 points each frame. Solved is 200 points. Landing outside landing pad is possible. Fuel is infinite, so an agent can learn to fly and then land on its first attempt. Action is two real values vector from -1 to +1. First controls main engine, -1..0 off, 0..+1 throttle from 50% to 100% power. Engine can't work with less than 50% power. Second value -1.0..-0.5 fire left engine, +0.5..+1.0 fire right engine, -0.5..0.5 off.
  • LunarLanderContinuous-v2 defines "solving" as getting average reward of 200 over 100 consecutive trials.
User Episodes before solve Write-up Video
Keavnn 30 writeup
timurgepard 40 (Symphony🎹 ver 2.1) without exploration episodes writeup video
liu 57 (Average:100) Write-up
timurgepard 90 (Symphony🎹 ver 2.0) without exploration episodes writeup video
BS Haney 100 Write-up YouTube
Mathias Åsberg 🔥 178 writeup Video
Nick Kaparinos 300 Write-up gif
shnippi 422 writeup
Nandino Cakar 474 writeup
Felix Nica 556 Write-up YouTube
Nikhil Barhate 1500 Write-up GIF
Jootten 2472 Write-up YouTube
Tom 5000 Write-up YouTube
Sigve Rokenes 5300 Write-up GIF

BipedalWalker-v2 and BipedalWalker-v3

Reward is given for moving forward, total 300+ points up to the far end. If the robot falls, it gets -100. Applying motor torque costs a small amount of points, more optimal agent will get better score. State consists of hull angle speed, angular velocity, horizontal speed, vertical speed, position of joints and joints angular speed, legs contact with ground, and 10 lidar rangefinder measurements. There's no coordinates in the state vector.
  • Environment Details
  • BipedalWalker-v2 defines "solving" as getting average reward of 300 over 100 consecutive trials.
  • by @olegklimov
User Version Episodes before solve Write-up Video
timurgepard 3.0 28 (Symphony🤖 ver 3.0) writeup video
timurgepard 3.0 40 (Symphony🎹 ver 2.0) writeup video
Benjamin & Thor 3.0 57 (TRPO with OU action noise) writeup
timurgepard 3.0 100 (Monte-Carlo🌊 & Temporal Difference🔥) writeup
Lauren 2.0 110 writeup Video
Mathias Åsberg 😎 2.0 164 writeup Video
liu 2.0 200 (AverageEpRet:338) writeup
Nandino Cakar 3.0 474 writeup
Yoggi Voltbro 3.0 696 write-up video
Nikhil Barhate 2.0 800 writeup gif
Nick Kaparinos 3.0 800 Write-up gif
Vinit & Abhimanyu 2.0 910 writeup Video
shnippi 3.0 925 writeup
M 2.0 960 writeup Video
mayurmadnani 2.0 1000 Write-up Youtube
Rafael1s 2.0 1795 Write-up Youtube
chitianqilin 2.0 47956 writeup Youtube
ZhiqingXiao 3.0 0 (use close-form preset policy) writeup
koltafrickenfer 2.0 N/A writeup youtube
alirezamika 2.0 N/A writeup
404akhan 2.0 N/A writeup
Udacity DRLND Team 2.0 N/A writeup gif

BipedalWalkerHardcore-v2 and BipedalWalkerHardcore-v3

Hardcore version with ladders, stumps, pitfalls. Time limit is increased due to obstacles. Reward is given for moving forward, total 300+ points up to the far end. If the robot falls, it gets -100. Applying motor torque costs a small amount of points, more optimal agent will get better score. State consists of hull angle speed, angular velocity, horizontal speed, vertical speed, position of joints and joints angular speed, legs contact with ground, and 10 lidar rangefinder measurements. There's no coordinates in the state vector.
  • BipedalWalkerHardcore-v2 defines "solving" as getting average reward of 300 over 100 consecutive trials.
User Version Episodes before solve 100-Episode Average Score Write-up Video
honghaow 3.0 3593 312.10 write-up video
Yoggi Voltbro 3.0 7280 302.92 ± 10.82 write-up video
Nick Kaparinos 3.0 15500 305.40 ± 21.35 Write-up gif
liu 2.0 N/A 319 (average of 10000 trials) writeup
DollarAkshay 2.0 N/A N/A writeup
ryogrid 2.0 N/A N/A writeup
dgriff777 2.0 N/A 300 writeup video
lerrytang and hardmaru 2.0 N/A 300 writeup video
hardmaru 2.0 N/A 313 ± 53 writeup video
Alister Maguire 3.0 N/A 313 Write-up gif

CarRacing-v0

Easiest continuous control task to learn from pixels, a top-down racing environment. Discreet control is reasonable in this environment as well, on/off discretisation is fine. State consists of 96x96 pixels. Reward is -0.1 every frame and +1000/N for every track tile visited, where N is the total number of tiles in track. For example, if you have finished in 732 frames, your reward is 1000 - 0.1*732 = 926.8 points. Episode finishes when all tiles are visited. Some indicators shown at the bottom of the window and the state RGB buffer. From left to right: true speed, four ABS sensors, steering wheel position, gyroscope.
  • by @olegklimov
  • CarRacing-v0 defines "solving" as getting average reward of 900 over 100 consecutive trials.
User Episodes before solve 100-Episode Average Score Write-up Video
irvpet N/A 913 ± 26 writeup video
lmclupr N/A N/A writeup
IPAM-AMD 900 907 ± 24 writeup Video
hardmaru N/A 906 ± 21 writeup Videos
Rafael1s 2760 901 (*) writeup Video
sebastianrisi N/A 903 ± 72 writeup video
ctallec N/A 870 ± 120 writeup video
agaier and hardmaru N/A 893 ± 74 writeup video
jperod N/A 905 ± 24 writeup Video
JinayJain N/A 909 ± 10 writeup video

(*) They used reward shaping (added some score back when the agent dies) during training to make training work better, but unfortunately kept the artificial shaped score for evaluation. When testing their agent using their model (and also trying to train it from scratch, which performed worse), we got a score of 820. We have filed an issue. We found a similar problem with another PPO repo here.

CarRacing-v1

v1: Changed track completion logic and added domain randomization (0.24.0)

User Episodes before solve 100-Episode Average Score Write-up Video
Ray Coden Mercurius 925 917 writeup video

MuJoCo

Inverted Pendulum

This environment involves a cart that can be moved linearly, with a pole fixed on it at one end and having another end free. The cart can be pushed left or right, and the goal is to balance the pole on the top of the cart by applying forces on the cart.

User Episode 100-Episode Average Score Write-up Video
timurgepard 56 1000.0 (Symphony🎹 ver 2.0) writeup

Walker2d-v1 and Walker 2d-v2

Make a two-dimensional bipedal robot walk forward as fast as possible.
  • Walker2d-v1 is an unsolved environment, which means it does not have a specified reward threshold at which it's considered solved.
  • The robot model is based on work by Erez, Tassa, and Todorov [Erez11].
User Episode 100-Episode Average Score Write-up Video
timurgepard 500 7920.0 (ep limit 2000 steps) (Symphony🎹 ver 2.0) writeup video
timurgepard 450 7670.0 (ep limit 2000 steps) (Symphony🎹 ver 2.0) writeup video
zlw21gxy N/A 7197.15 writeup
pat-coady N/A 7167.24 link video
joschu N/A 5594.75 link video
Nick Kaparinos N/A 5317.38 ± 15.86 Write-up gif
songrotek N/A 1222.12 link video
BS Haney N/A 1190 Write-up YouTube

Ant-v1

Make a four-legged creature walk forward as fast as possible.
  • Ant-v1 defines "solving" as getting average reward of 6000.0 over 100 consecutive trials.
  • This task originally appeared in [Schulman15].
User Episode 100-Episode Average Score Write-up Video
timurgepard 700 9320.0 (ep limit 2000 steps) (Symphony🎹 ver 2.0) writeup video
zlw21gxy 1000 N/A writeup
pat-coady 69154 N/A writeup
joschu N/A N/A writeup

HalfCheetah-v4

half_cheetah Make a 2-dimensional robot walk forward as fast as possible.
  • The HalfCheetah is a 2-dimensional robot consisting of 9 body parts and 8 joints connecting them (including two paws).
  • The goal is to apply a torque on the joints to make the cheetah run forward (right) as fast as possible.
  • This environment is based on the work by P. Wawrzyński
User Episodes before solve Write-up Video
timurgepard 25 (Symphony🎹 ver 2.0) writeup video
tareknaser N/A writeup video

Humanoid-v4

Make 3D humanoid robot walk forward as fast as possible.
  • Humanoid-v4 is an unsolved environment, which means it does not have a specified reward threshold at which it's considered solved.
  • The 3D bipedal robot is designed to simulate a human. Humanoid-v4 defines "solving" as acquiring human like motions.
  • The robot model is based on work by Tassa, Erez, and Todorov [Tassa12].*
User Episodes before solve 100-Episode Average Score Write-up Video
timurgepard 1500 12,600.0 (ep limit 2000 steps) (Symphony🎹 ver 2.0) writeup video

HumanoidStandup-v4

Make the humanoid standup and then keep it standing by applying torques on the various hinges.

  • The environment starts with the humanoid laying on the ground, and then the goal of the environment is to make the humanoid standup and then keep it standing by applying torques on the various hinges.
  • The 3D bipedal robot is designed to simulate a human. It has a torso (abdomen) with a pair of legs and arms. The legs each consist of two links, and so the arms (representing the knees and elbows respectively).
  • This environment is based on the environment introduced by Tassa, Erez and Todorov in “Synthesis and stabilization of complex behaviors through online trajectory optimization”.
User Episodes before solve 100-Episode Average Score Write-up Video
timurgepard 3200 (step 960k, ep steps 300) ~320000.0 (Symphony🎹 ver 2.0) writeup video

Pusher-v4

“Pusher” is a multi-jointed robot arm which is very similar to that of a human. The goal is to move a target cylinder (called object) to a goal position using the robot’s end effector (called fingertip). The robot consists of shoulder, elbow, forearm, and wrist joints.

User Episodes before solve 100-Episode Average Score Write-up Video
timurgepard 350 -45.0 (Symphony🎹 ver 2.0) writeup video

Swimmer-v4

The swimmer consist of three segments ('links') and two articulation joints (’rotors’) - one rotor joint connecting exactly two links to form a linear chain. The swimmer is suspended in a two dimensional pool, and the goal is to move as fast as possible towards the right by applying torque on the rotors and using the fluids friction.

User Episodes before solve 100-Episode Average Score Write-up Video
timurgepard 55 205.0 (Symphony🎹 ver 2.0) writeup

PyGame Learning Environment

FlappyBird-v0

This environment adapts a game from the PyGame Learning Environment (PLE). To run it, you will need to install gym-ple from https://github.com/lusob/gym-ple.

Flappybird is a side-scrolling game where the agent must successfully navigate through gaps between pipes. The up arrow causes the bird to accelerate upwards. If the bird makes contact with the ground or pipes, or goes above the top of the screen, the game is over. For each pipe it passes through it gains a positive reward of +1. Each time a terminal state is reached it receives a negative reward of -1.

  • FlappyBird-v0 is an unsolved environment, which means it does not have a specified reward threshold at which it's considered solved.
  • by @lusob
User Best 100-episode performance Write-up Video
dguoy 264.0 ± 0.0 writeup video
andreimuntean 261.12 ± 2.61 writeup
Kunal Arora 90.83 writeup
chuchro3 62.26 ± 7.81 writeup
warmar 11.28 ± 14.25 writeup video1 video2

Snake-v0

Snake is a game where the agent must maneuver a line which grows in length each time food is touched by the head of the segment. The line follows the previous paths taken which eventually become obstacles for the agent to avoid.

The food is randomly spawned inside of the valid window while checking it does not make contact with the snake body.

User Best 100-episode performance Write-up Video
carsonprindle .44 ± .04 writeup

Atari Games

Atlantis-v0

User Best 100-episode performance Write-up
msemple1111 62,500 ± 0 writeup

Breakout-v0

User Best 100-episode performance Write-up
ppwwyyxx 760.07 ± 18.37 writeup

Pong-v5

User Best 100-episode performance Write-up
Nick Kaparinos 21.00 ± 0.00 Write-up
ppwwyyxx 20.81 ± 0.04 writeup

MsPacman-v0

User Best 100-episode performance Write-up
ppwwyyxx 5738.30 ± 171.99 writeup

SpaceInvaders-v0

User Best 100-episode performance Write-up
ppwwyyxx 3454.00 ± 0 writeup

Seaquest-v0

User Best 100-episode performance Write-up
ppwwyyxx 50209 ± 2440.07 writeup

Toy text

Simple text environments to get you started.

Taxi-v2

This task was introduced in [Dietterich2000] to illustrate some issues in hierarchical reinforcement learning. There are 4 locations (labeled by different letters) and your job is to pick up the passenger at one location and drop him off in another. You receive +20 points for a successful dropoff, and lose 1 point for every timestep it takes. There is also a 10 point penalty for illegal pick-up and drop-off actions.

[Dietterich2000] T Erez, Y Tassa, E Todorov, "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition", 2011.

User 100 Episodes Best Average Reward Write-up Video Solved In Episode
Michael Schock 9.716 writeup 19790
giskmov 9.700 writeup
Hari Iyer 9.634 writeup
Jin.P 9.617 writeup
jo4x962k7JL 9.600 writeup
Delton Oliver 9.59 writeup
Eka Kurniawan 9.59 writeup
Daniel T. Plop 9.582 writeup
Roald Brønstad 9.574 writeup
andyharless 9.57 writeup
ksankar 9.530 writeup
Tom Roth 9.500 writeup
mostoo45 9.492 writeup
crazyleg 9.49 writeup
Akshay Sathe 9.471 writeup
Ridhwan Luthra 9.461 writeup 15000
newwaylw 9.459 writeup 20000
romOlivo 9.449 writeup
Herimiaina ANDRIA-NTOANINA 9.446 writeup
aleckretch 9.426 writeup
Cihan Soylu 9.423 writeup
Tristan Frizza 9.358 writeup
Jhon Muñoz 9.334 writeup
Mahaveer Jain 9.296 writeup
Mostafa Elhoushi 9.2926 writeup
Rajiv Krishnakumar 9.277 writeup 20000
Brungi Vishwa Sourab 9.23 writeup

Taxi-v3

This task was introduced in [Dietterich2000] to illustrate some issues in hierarchical reinforcement learning. There are 4 locations (labeled by different letters) and your job is to pick up the passenger at one location and drop him off in another. You receive +20 points for a successful dropoff, and lose 1 point for every timestep it takes. There is also a 10 point penalty for illegal pick-up and drop-off actions.

[Dietterich2000] T Erez, Y Tassa, E Todorov, "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition", 2011.

User 100 Episodes Best Average Reward Write-up Video Solved In Episode
andyharless 9.26 writeup
chillage 9.249 writeup
morakanhan 9.247 writeup 20000
yurkovak 9.19 writeup 20000
crazyleg 9.07 writeup
rahulkaplesh 8.97 writeup+Notebook 20000
Mattia-Scarpa 8.83 writeup 20000
Tiger37 8.8 writeup 20000
take2rohit 8.57 writeup+Notebook video 5000

GuessingGame-V0

The goal of the game is to guess within 1% of the randomly chosen number within 200 time steps

After each step the agent is provided with one of four possible observations which indicate where the guess is in relation to the randomly chosen number

User Average Episode Steps Write-up Video Solved In Episode
Anandha Krishnan H 51 (use close-form preset policy) writeup
Britto Sabu 53 (use close-form preset policy) writeup

FrozenLake-v0

The agent controls the movement of a character in a grid world. Some tiles of the grid are walkable, and others lead to the agent falling into the water. Additionally, the movement direction of the agent is uncertain and only partially depends on the chosen direction. The agent is rewarded for finding a walkable path to a goal tile.

User Episodes Before Solve Write-up Video Solved In Episode
Nitish tom michael 100 writeup

FrozenLake8x8-v0

The agent controls the movement of a character in a grid world. Some tiles of the grid are walkable, and others lead to the agent falling into the water. Additionally, the movement direction of the agent is uncertain and only partially depends on the chosen direction. The agent is rewarded for finding a walkable path to a goal tile.

User 100 Episodes Best Average Reward Write-up Video Solved In Episode
Sukesh Shenoy 85 writeup