6 datasets found

This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL).

Conservative data sharing for multi-task offline reinforcement learning.

Offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected...

D4RL benchmark dataset, which consists of four offline logging datasets, collected by different one or mixed behavior policies.

D4RL datasets for maze2d-umaze, maze2d-medium, maze2d-large, antmaze-umaze, antmaze-medium, antmaze-large, parking, soccer-sim, and soccer-physical tasks

The dataset used in the paper is a collection of simulated environments, including blockworld, canada, cart, cartpole, cartpoleBalance, cartpoleParallelDouble,...