Title: RTG: Reverse Trajectory Generation for Reinforcement Learning Under Sparse Reward
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Physics Simulation

Abstract: Deep Reinforcement Learning (DRL) under sparse reward conditions remains a long-standing challenge in robotic learning. In such settings, extensive exploration is often required before meaningful reward signals can guide the propagation of state-value functions. Prior approaches typically rely on offline demonstration data or carefully crafted curriculum learning strategies to improve exploration efficiency. In contrast, we propose a novel method tailored to rigid body manipulation tasks that addresses sparse reward without the need for guidance data or curriculum design. Leveraging recent advances in differentiable rigid body dynamics and trajectory optimization, we introduce the Reverse Rigid-Body Simulator (RRBS), a system capable of generating simulation trajectories that terminate at a user-specified goal configuration. This reverse simulation is formulated as a trajectory optimization problem constrained by differentiable physical dynamics. RRBS enables the generation of physically plausible trajectories with known goal states, providing informative guidance for conventional RL in sparse reward environments. Leveraging this, we present Reverse Trajectory Generation (RTG), a method that integrates RRBS with a beam search algorithm to produce reverse trajectories, which augment the replay buffer of off-policy RL algorithms like DDQN to solve the sparse reward problem. We evaluate RTG across various rigid body manipulation tasks, including sorting, gathering, and articulated object manipulation. Experiments show that RTG significantly outperforms vanilla DRL and improved sampling strategies like Hindsight Experience Replay (HER) and Reverse Curriculum Generation (RCG). Specifically, RTG is the only method that can solve each task with success rates of over 70% within given compute budget.

TL;DR: A sample-efficient off-policy DRL method which leverages trajectory-optimization-based reverse simulator to generate reverse trajectories that terminate at high-reward states to solve the sparse reward problem