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2D Environment
The dataset used in the paper is a 2D environment where experiments are done. -
MuJoCo Environment
The dataset used in the paper is a MuJoCo environment, with 13-states and 4-control inputs, nonlinear dynamics with polynomial dependency in the control inputs. -
Guard: A safe reinforcement learning benchmark
The dataset used in the paper is a collection of robot locomotion tasks with various constraints. -
Pretrained Visual Representations in Reinforcement Learning
Visual reinforcement learning (RL) has made significant progress in recent years, but the choice of visual feature extractor remains a crucial design decision. -
HandManipulateBlock
The HandManipulateBlock environment from OpenAI gym robotics suite -
FetchPickAndPlace and HandManipulateBlock
The FetchPickAndPlace and HandManipulateBlock environments from OpenAI gym robotics suite -
FetchPush, FetchPickAndPlace and HandManipulateBlock
The FetchPush, FetchPickAndPlace and HandManipulateBlock environments from OpenAI gym robotics suite -
MuJoCo environments
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used MuJoCo environments from the OpenAI gym. -
Funnel board
The Funnel board task is a domain where a ball falls through a grid of obstacles onto one of five platforms. Every other row of obstacles consists of funnel-shaped objects,... -
Room runner
The Room runner task is a domain where an agent moves through a randomly generated map of rooms, which are observed in 2D from above. The agent follows the policy of always... -
Event Camera-based Reinforcement Learning
The dataset used in the paper is a simulated environment for event camera-based reinforcement learning. The dataset includes a car-like robot equipped with an event camera, and... -
Mujoco control tasks
The authors used the Mujoco control tasks, including Ant-v2, HalfCheetah-v2, Hopper-v2, and Walker2d-v2. -
Multiple-confounded-Mujoco-Envs
The dataset used in the paper is a collection of environments with multiple confounders, including mass, length, damping, and a crippled leg. The dataset is used to evaluate the... -
Four Rooms domain
The Four Rooms domain is a classic reinforcement learning environment where an agent must navigate a grid world to reach one of four goals. -
Simulated Self-Driving Car Environment
The dataset used in the paper is a simulated self-driving car environment, a mountain car environment, and a robotic environment. -
Self-supervised Relational RL with Independently Controllable Subgoals
The dataset used in the paper is a multi-object environment with a robotic arm and multiple objects to manipulate. The agent learns to control the objects independently and... -
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
