# Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games

arXiv: https://arxiv.org/abs/2010.03956 · HTML: https://mlsanity.com/paper/2010.03956/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Transfer Dynamics in Emergent Evolutionary Curricula](https://mlsanity.com/paper/2203.10941/) (2022)
- [Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents](https://mlsanity.com/paper/2104.10610/) (2021)
- [Minding Motivation: The Effect of Intrinsic Motivation on Agent Behaviors](https://mlsanity.com/paper/2507.19725/) (2025)

## Abstract (arXiv)

Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward. To tackle this problem, a common approach is to use reward shaping to help exploration. However, an important drawback of reward shaping is that agents sometimes learn to optimize the shaped reward instead of the true objective. In this paper, we present a novel technique that we call action guidance that successfully trains agents to eventually optimize the true objective in games with sparse rewards while maintaining most of the sample efficiency that comes with reward shaping. We evaluate our approach in a simplified real-time strategy (RTS) game simulator called μRTS.