A Study on Application of Curriculum Learning in Deep Reinforcement Learning : Action Acquisition in Shooting Game AI as Example

2021 ◽  
Author(s):  
Ikumi Kodaka ◽  
Fumiaki Saitoh
Keyword(s):  
Reinforcement Learning ◽  
Game Ai

Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Game-AI Research Platform

2021 ◽  
pp. 503-562
Author(s):  
Adil Khan ◽  
Muhammad Naeem ◽  
Asad Masood Khattak ◽  
Muhammad Zubair Asghar ◽  
Abdul Haseeb Malik
Keyword(s):  
Reinforcement Learning ◽  
Game Ai ◽  
Research Platform

scholarly journals Playing Card-Based RTS Games with Deep Reinforcement Learning

2019 ◽  
Author(s):  
Tianyu Liu ◽  
Zijie Zheng ◽  
Hongchang Li ◽  
Kaigui Bian ◽  
Lingyang Song
Keyword(s):  
Reinforcement Learning ◽  
Decision Tree ◽  
State Space ◽  
Imperfect Information ◽  
Rule Based ◽  
Playing Card ◽  
Board Games ◽  
Deep Model ◽  
Game Ai ◽  
Rts Game

Game AI is of great importance as games are simulations of reality. Recent research on game AI has shown much progress in various kinds of games, such as console games, board games and MOBA games. However, the exploration in RTS games remains a challenge for their huge state space, imperfect information, sparse rewards and various strategies. Besides, the typical card-based RTS games have complex card features and are still lacking solutions. We present a deep model SEAT (selection-attention) to play card-based RTS games. The SEAT model includes two parts, a selection part for card choice and an attention part for card usage, and it learns from scratch via deep reinforcement learning. Comprehensive experiments are performed on Clash Royale, a popular mobile card-based RTS game. Empirical results show that the SEAT model agent makes it to reach a high winning rate against rule-based agents and decision-tree-based agent.

Playing First-Person Perspective Games with Deep Reinforcement Learning Using the State-of-the-Art Game-AI Research Platforms

2021 ◽  
pp. 635-667
Author(s):  
Adil Khan ◽  
Asad Masood Khattak ◽  
Muhammad Zubair Asghar ◽  
Muhammad Naeem ◽  
Aziz Ud Din
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
First Person ◽  
Person Perspective ◽  
Game Ai ◽  
First Person Perspective ◽  
Art Game

Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning

2019 ◽  
Vol 14 (3) ◽  
pp. 8-18 ◽  
Author(s):  
Nicolas A. Barriga ◽  
Marius Stanescu ◽  
Felipe Besoain ◽  
Michael Buro
Keyword(s):  
Reinforcement Learning ◽  
Policy Learning ◽  
Game Ai ◽  
Rts Game

scholarly journals Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Adil Khan ◽  
Jiang Feng ◽  
Shaohui Liu ◽  
Muhammad Zubair Asghar
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Computer Games ◽  
Learning Experience ◽  
Research Work ◽  
Real Life ◽  
Basic Scenario ◽  
Final Performance ◽  
Game Ai ◽  
Research Platform

These days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). While training, the speed of the learning agent greatly depends on the number of frames the agent is permitted to skip. In this paper, how the frame skipping rate influences the agent’s learning and final performance is proposed, particularly using deep Q-learning, experience replay memory, and the ViZDoom Game AI research platform. The agent is trained and tested on Doom’s basic scenario(s) where the results are compared and found to be 10% better compared to the existing state-of-the-art research work on Doom-based agents. The experiments show that the profitable and optimal frame skipping rate falls in the range of 3 to 11 that provides the best balance between the learning speed and the final performance of the agent which exhibits human-like behavior and outperforms an average human player and inbuilt game agents.

scholarly journals Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning

2020 ◽  
Author(s):  
Ruimin Shen ◽  
Yan Zheng ◽  
Jianye Hao ◽  
Zhaopeng Meng ◽  
Yingfeng Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Domain Knowledge ◽  
Behavioral Characteristic ◽  
Multi Objective ◽  
Behavioral Diversity ◽  
Game Ai ◽  
Game Industry ◽  
Characteristic Style ◽  
New Framework

Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the domain knowledge and is hard to achieve manually. Deep reinforcement learning sheds light on advancing the automatic Game AI design. However, most of them focus on creating a superhuman Game AI, ignoring the importance of behavioral diversity in games. To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles with almost no domain knowledge. More importantly, EMOGI succeeds in creating a range of diverse styles, providing behavior-diverse Game AIs. Evaluations on the Atari and real commercial games indicate that, compared to existing algorithms, EMOGI performs better in generating diverse behaviors and significantly improves the efficiency of Game AI design.

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