Classic Control

Classic Control environments provide a foundational platform for exploring and experimenting with RL algorithms. These environments are designed to be simple, allowing researchers and practitioners to focus on the fundamental principles of control without the complexities of high-dimensional and physics-based simulations.

Step-Based Environments

Name	Description	Horizon	Action Dimension	Observation Dimension
`fancy/SimpleReacher-v0`	Simple reaching task (2 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory.	200	2	9
`fancy/LongSimpleReacher-v0`	Simple reaching task (5 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory.	200	5	18
`fancy/ViaPointReacher-v0`	Simple reaching task leveraging a via point, which supports self collision detection. Provides a reward only at 100 and 199 for reaching the viapoint and goal point, respectively.	200	5	18
`fancy/HoleReacher-v0`	5 link reaching task where the end-effector needs to reach into a narrow hole without collding with itself or walls.	200	5	18

MP Environments

Name	Description	Horizon	Action Dimension
`fancy_DMP/ViaPointReacher-v0`	A DMP provides a trajectory for the `fancy/ViaPointReacher-v0` task.	200	25
`fancy_DMP/HoleReacherFixedGoal-v0`	A DMP provides a trajectory for the `fancy/HoleReacher-v0` task with a fixed goal attractor.	200	25
`fancy_DMP/HoleReacher-v0`	A DMP provides a trajectory for the `fancy/HoleReacher-v0` task. The goal attractor needs to be learned.	200	30