Starcraft II: A New Challenge For Reinforcement Learning

Abstract

This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main gam

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyvinyals2017starcraft

Related papers

• SCC: An Efficient Deep Reinforcement Learning Agent Mastering The Game Of Starcraft II (2020)0.00
• Tstarbots: Defeating The Cheating Level Builtin AI In Starcraft II In The Full Game (2018)0.00
• Starcraft Micromanagement With Reinforcement Learning And Curriculum Transfer Learning (2018)16.19
• Applying Supervised And Reinforcement Learning Methods To Create Neural-network-based Agents For Playing Starcraft II (2021)0.00
• Adversary Agent Reinforcement Learning For Pursuit-evasion (2021)0.00
• Macro Action Selection With Deep Reinforcement Learning In Starcraft (2018)9.92
• Tstarbot-x: An Open-sourced And Comprehensive Study For Efficient League Training In Starcraft II Full Game (2020)0.00
• Episodic Exploration For Deep Deterministic Policies: An Application To Starcraft Micromanagement Tasks (2016)0.00