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This repo relates to the survey paper <Goal-Conditioned Reinforcement Learning: Problems and Solutions>. We collects widely used benchmark environments and conclude a series of research works for goal-conditioned reinforcement learning (GCRL).

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GCRL-Collection

This repo relates to the survey paper <Goal-Conditioned Reinforcement Learning: Problems and Solutions>. We collects widely used benchmark environments and conclude a series of research works for goal-conditioned reinforcement learning (GCRL).

Summary of collected algorithms

Approach Resolved Goal Type Sub-Goal Relabel Optimization
UVFA Vector/Image
HER Vector/Image
Laplacian Representation Learning Vector/Image
SR Vector/Image
DDL Vector/Image
SoRB Vector/Image
Skew-Fit Vector
HER with Demonstrations Vector
GoalGAN Vector
Sub-goal Discovery Vector
HGG Vector
CHER Vector
G-HER Vector
Hindsight Planner Vector
GDP Vector
VDS Vector
MEGA Vector
PlanGAN Vector
AIM Vector
I-HER Vector
GCSL Vector
MapGo Vector
L3P Vector
RIG Image
DISCERN Image
HVF Image
AMIGo Image
GAP Image
LEXA Image

Widely used benchmark environments.

Update: Most of these environments has been integrated in Gymnasium-Robotics.

Note almost all environments support various kinds of goal structures, i.e., vector, image, language, etc.

Environment Benchmark Type Common Used Goal Structure Description
FetchReach Gym Robotics/Mujoco Vector/Image A robotic arm reaches a target position
FetchPush Gym Robotics/Mujoco Vector/Image A robotic arm pushes a block to a target position
FetchPickAndPlace Gym Robotics/Mujoco Vector/Image A robotic arm first picks up the block then places it to a target position
FetchSlide Gym Robotics/Mujoco Vector/Image A robotic arm slides a buck to a goal position
HandManipulateBlock Gym Robotics/Mujoco Vector/Image Orient a block using a robot hand
HandManipulateEgg Gym Robotics/Mujoco Vector/Image Orient an egg using a robot hand
HandManipulatePen Gym Robotics/Mujoco Vector/Image Orient a pen using a robot hand
HalfCheetah Mujoco Vector/Image Make a 2D cheetah robot run and keep a specific speed
AntLocomotion/Ant Maze Mujoco Vector Make a 3D four-legged robot walk to a target position
Sawyer Mujoco Vector A sawyer robot reaches a target position
Reacher Mujoco Vector/Image A reacher robot reaches a target position
N-D Mass Mujoco Vector A ball reaches a target position
DoorOpening Mujoco Image A sawyer robot opens a door by latching onto the handle
Dy-Reaching Mujoco Image A robotic arm reaches a target position moving along a straight line
Dy-Circling Mujoco Image A robotic arm reaches a target position moving along a circle
Dy-Pushing Mujoco Image A robotic arm moves the box to a moving target position
Dy-Pouring Mujoco Image A robotic arm grips the can and pours the water into a cup
WaterMaze DeepMind Lab Image Get to a target position described by an image
Seaquest Atari Image Get to a target position described by an image
Montezuma’s Revenge Atari Image Get to a target position described by an image
Reacher DeepMind Control Suit Image A two-link planar reacher poses an aimed shape
Manipulator DeepMind Control Suit Image A 3-DoF finger robot rotates the body into a target orientation
Finger DeepMind Control Suit Image A planar manipulator brings an object in a target location
Place Object in Tray PyBullet Image A robotic arm puts toys into a tray
Opening Drawer PyBullet Image A robotic arm opens the drawer
PickandPlace PyBullet Image A robotic arm first picks up the block then places it to a target position
Five Object Manipulation CLEVR-Robot/Mujoco Image/Language A point mass agent arranges, orders and sorts 5 fixed objects
Diverse Object Manipulation CLEVR-Robot/Mujoco Image/Language A point mass agent orders objects in different shapes and colors
Playground Playground Image/Language A 2D hand moves and grasps/releases different objects
VizDoom ViZDoom Image/Language AI bots reach specific place in the first person shooter 3D game Doom

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This repo relates to the survey paper <Goal-Conditioned Reinforcement Learning: Problems and Solutions>. We collects widely used benchmark environments and conclude a series of research works for goal-conditioned reinforcement learning (GCRL).

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