The Mechanism Matters: When Knowledge Graphs Help Reinforcement Learning
Knowledge graphs (KGs) are widely used to inject prior knowledge into reinforcement learning (RL), yet the literature is dominated by single-domain, positive-result method papers, so we lack a systematic account of when KG structure helps an agent, when it is neutral, and when it hurts. We conduct a controlled study that independently varies the RL task, the injection mechanism (state features, action masking, or potential-based reward shaping), and KG quality. Using a synthetic, fully controllable KG over MiniGrid environments, we report three findings. First, on compositional sparse-reward tasks structured KG guidance improves sample efficiency and solve reliability (70% to 97% of seeds...
| Authors | Mohammed Sameer Syed |
|---|---|
| Primary category | cs.LG |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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