# Multi-Task Reinforcement Learning

In heterogeneous multi-task offline reinforcement learning, tasks differ in state dimension, action space and dynamics, so a directly shared backbone hits both interface mismatch and gradient conflict. HeteroGrad-DT gives each task its own input tokenizer and prediction head around a shared sequence backbone, and a Gradient Flow Transformer pools per-task gradients over a synchronization interval and learns fusion weights to coordinate the shared update. On the DeepMind Control Suite with up to 30 tasks, its average normalized return exceeds representative multi-task methods, with a 32.3% relative gain over standard shared-backbone training and 8.6% over a PCGrad baseline using the same task-aware interfaces.

In heterogeneous multi-task offline reinforcement learning, tasks differ in state dimension, action space and dynamics, so a directly shared backbone hits both interface mismatch and gradient conflict. HeteroGrad-DT gives each task its own input tokenizer and prediction head around a shared sequence backbone, and a Gradient Flow Transformer pools per-task gradients over a synchronization interval and learns fusion weights to coordinate the shared update. On the DeepMind Control Suite with up to 30 tasks, its average normalized return exceeds representative multi-task methods, with a 32.3% relative gain over standard shared-backbone training and 8.6% over a PCGrad baseline using the same task-aware interfaces.

AlphaNIST trains a single reinforcement-learning policy across all 20 assembly tasks on the NIST Assembly Task Board 1 using only a binary success reward, with no demonstrations, progress shaping, scripted primitives, or per-task tuning. Prior learning-based methods typically solved one task at a time or relied on fixed sequences, staged initial poses, and dense reward guidance. In simulation, the shared policy reaches 98.2% overall training success and outperforms separately trained specialists on small-part tasks.

AlphaNIST trains a single reinforcement-learning policy across all 20 assembly tasks on the NIST Assembly Task Board 1 using only a binary success reward, with no demonstrations, progress shaping, scripted primitives, or per-task tuning. Prior learning-based methods typically solved one task at a time or relied on fixed sequences, staged initial poses, and dense reward guidance. In simulation, the shared policy reaches 98.2% overall training success and outperforms separately trained specialists on small-part tasks.