Published August 5, 2025
| Version v1
Dataset
Open
Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
Authors/Creators
Description
The dataset contains data collected for 1 million time steps across different RL environments in Mujoco Playground using trained-agents with actions sampled from the learnt Gausian distribution.
Here only the data for the following environments are released as an example - CartpoleBalance, CheetahRun, FingerSpin
Each environment specific folder contains .npz file which has the following keys described in the table below (here num_timesteps=1e6)
| Key | Definition |
| states | True state values, including initial and terminal states, with shape (num_timesteps x state_dimension) |
| actions | Action values with shape (num_timesteps x action_dimension) |
| state_jacobians | Jacobian matrices representing the derivative of the next true state with respect to the current state, with shape (num_timesteps x state_dimension x state_dimension) |
| action_jacobians | Jacobian matrices representing the derivative of the next true state with respect to the action, with shape (num_timesteps x state_dimension x action_dimension) |
| obs | Observation values with shape (num_timesteps x obs_dim) |
|
rewards |
Reward values with shape (num_timesteps x 1) |
|
dones |
Done Indicator Vector: A binary vector of shape (num_timesteps, 1) indicating episode terminations. A value of 1 marks a timestep where an episode ends. The corresponding index after this in the states data represents the terminal state. At this terminal index, all keys except for "observation" are set to -1. |
|
total_steps_collected |
The total number of timesteps collected |