Deep Reinforcement Learning researches in RTS and MOBA games (Around 2020)

Source: Team Liquid Website
Source: deepmind.com
Source: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Source: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Source: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Source: Grandmaster level in StarCraft II using multi-agent reinforcement learning
  1. Main agent: The one to be deployed
  2. League Exploiter: To find common weaknesses from past iterations in the League
  3. Main Exploiter: To find common weakness of the current main agent
Win rate and EPM collected by Race
Source: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Source: Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
Source: Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
  1. The AI Server with game environments: A cluster of servers with game environments, agents, and gain information with softmax
  2. Dispatch module: Gather information such as penalty, feature values, etc. and pack the gathered information before sending it to the memory pool
  3. Memory Pool: A server running a memory efficient circular queue.
  4. Reinforcement Learner: A distributed training environment that pulls information from the memory pool and does RL training. Information is shared through RAM to achieve faster transmission. The updated weights and award/penalty are sent directly back to the AI server.
Source: Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
Source: Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

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Aspiring computer scientist

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Junyang Jin

Junyang Jin

Aspiring computer scientist

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