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Improving Bots Playing Starcraft II Game in PySC2 Environment
Krušina, Jan ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.
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Combat Management in Starcraft II Game by Means of Artificial Intelligence
Krajíček, Karel ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This thesis focuses on the use of Artificial Intelligence and design of working module in Real-Time Strategy (RTS) game, StarCraft II. The proposed solution uses Neural Network and Q-learning for combat management. For implementation, the StarCraft 2 Learning Environment has been used as a means of communication between the designed system and the game. Evaluation of the system is based on its ability to make progress over time.
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Machine Learning in Strategic Games
Vlček, Michael ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
Machine learning is spearheading progress for the field of artificial intelligence in terms of providing competition in strategy games to a human opponent, be it in a game of chess, Go or poker. A field of machine learning, which shows the most promising results in playing strategy games, is reinforcement learning. The next milestone for the current research lies in a computer game Starcraft II, which outgrows the previous ones in terms of complexity, and represents a potential new breakthrough in this field. The paper focuses on analysis of the problem, and suggests a solution incorporating a reinforcement learning algorithm A2C and hyperparameter optimization implementation PBT, which could mean a step forward for the current progress.
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Reinforcement Learning for Starcraft Game Playing
Chábek, Lukáš ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This work focuses on methods of machine learning for playing real-time strategy games. The thesis applies mainly methods of Q-learning based on reinforcement learning. The practical part of this work is implementing an agent for playing Starcraft II. Mine solution is based on 4 simple networks, that are colaborating together. Each of the network also teaches itself how to process all given actions optimally. Analysis of the system is based on experiments and statistics from played games.
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Combat Management in Starcraft II Game by Means of Artificial Intelligence
Krajíček, Karel ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This thesis focuses on the use of Artificial Intelligence and design of working module in Real-Time Strategy (RTS) game, StarCraft II. The proposed solution uses Neural Network and Q-learning for combat management. For implementation, the StarCraft 2 Learning Environment has been used as a means of communication between the designed system and the game. Evaluation of the system is based on its ability to make progress over time.
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Reinforcement Learning for Starcraft Game Playing
Chábek, Lukáš ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This work focuses on methods of machine learning for playing real-time strategy games. The thesis applies mainly methods of Q-learning based on reinforcement learning. The practical part of this work is implementing an agent for playing Starcraft II. Mine solution is based on 4 simple networks, that are colaborating together. Each of the network also teaches itself how to process all given actions optimally. Analysis of the system is based on experiments and statistics from played games.
|
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Machine Learning in Strategic Games
Vlček, Michael ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
Machine learning is spearheading progress for the field of artificial intelligence in terms of providing competition in strategy games to a human opponent, be it in a game of chess, Go or poker. A field of machine learning, which shows the most promising results in playing strategy games, is reinforcement learning. The next milestone for the current research lies in a computer game Starcraft II, which outgrows the previous ones in terms of complexity, and represents a potential new breakthrough in this field. The paper focuses on analysis of the problem, and suggests a solution incorporating a reinforcement learning algorithm A2C and hyperparameter optimization implementation PBT, which could mean a step forward for the current progress.
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|
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Improving Bots Playing Starcraft II Game in PySC2 Environment
Krušina, Jan ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.
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