High-level Strategy Selection Under Partial Observability In Starcraft: Brood War

Abstract

We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy. Here, a good strategy successfully counters the opponent's current and possible future strategies which can only be estimated using partial observations. We investigate whether we can utilize the full game state information during training time (in the form of an auxiliary prediction task) to increase performance. Experiments carried out within a StarCraft: Brood War bot against strong community bots show substantial win rate improvements over a fixed-strategy baseline and encouraging results when learning with the auxiliary task.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keygehring2018high

Related papers

• An Approach To Partial Observability In Games: Learning To Both Act And Observe (2021)3.58
• Macro Action Selection With Deep Reinforcement Learning In Starcraft (2018)9.92
• Hierarchical Reinforcement Learning In Starcraft II With Human Expertise In Subgoals Selection (2020)0.00
• Episodic Exploration For Deep Deterministic Policies: An Application To Starcraft Micromanagement Tasks (2016)0.00
• Starcraft Micromanagement With Reinforcement Learning And Curriculum Transfer Learning (2018)16.19
• Efficient Reinforcement Learning For Starcraft By Abstract Forward Models And Transfer Learning (2019)8.60
• SCC: An Efficient Deep Reinforcement Learning Agent Mastering The Game Of Starcraft II (2020)0.00
• Applying Supervised And Reinforcement Learning Methods To Create Neural-network-based Agents For Playing Starcraft II (2021)0.00