GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization

• URL: http://arxiv.org/abs/2606.03335v1
• Date: Tue, 02 Jun 2026 08:43:42 GMT
• Title: GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization
• Abstract summary: We propose a construction methodology for turning structured manipulation task families into GPU-parallel multi-task RL benchmarks.<n>The resulting benchmark supports simultaneous reinforcement learning over heterogeneous task suites.<n>We also propose DGPO, an on-policy demonstration guided method that combines importance weighted PPO with adaptive behavior cloning on matched demonstration actions.
• Score: 26.197318795235702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large scale GPU-parallel reinforcement learning has changed what can be trained in robot simulation, yet most systems still optimize one specialist policy per task. We propose a construction methodology for turning structured manipulation task families into GPU-parallel multi-task RL benchmarks, and instantiate it as MT-Libero using LIBERO assets and task predicates in Isaac Lab. The resulting benchmark supports simultaneous reinforcement learning over heterogeneous task suites with parallel rendering, physics randomization, and state-input or visual-input policies. To make such training practical under sparse success signals and limited prior data, we further propose DGPO, an on-policy demonstration guided method that combines importance weighted PPO with adaptive behavior cloning on matched demonstration actions. DGPO enables a tunable preference toward demonstrated task distributions, outperforming both prior-free RL and existing demonstration-based methods while preserving the stability and online improvement benefits of on-policy PPO.

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