202219807
commented
1 year ago Archives:
Dynamic Difficulty Adjustment AI for Dynamic Difficulty Adjustment in Games AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner ** DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints *** Dynamic Difficulty Adjustment via Fast User Adaptation Exploring Dynamic Difficulty Adjustment in Videogames Predicting Game Engagement and Difficulty Using AI Players
Multi Agent Systems https://marllib.readthedocs.io/en/latest/resources/awesome.html#magent https://github.com/LantaoYu/MARL-Papers#coordination http://rail.eecs.berkeley.edu/deeprlcourse-fa17/
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Counterfactual Multi-Agent Policy Gradients (COMA) Multi-Agent POsthumous Credit Assignment (MA-POCA)
Creating Intelligent Agents in Games National Academies of Sciences, Engineering, and Medicine. 2007. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2006 Symposium. Washington, DC: The National Academies Press. https://doi.org/10.17226/11827[.](https://nap.nationalacademies.org/read/11827/chapter/4) Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning Mediated Multi-Agent Reinforcement Learning Modeling Theory of Mind in Multi-Agent Games Using Adaptive Feedback Control On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring * Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning Towards a Unifying Model of Rationality in Multiagent Systems
Deep Reinforcement Learning A Survey of Deep Reinforcement Learning in Video Games Asynchronous Methods for Deep Reinforcement Learning Deep reinforcement learning from human preferences Deep Reinforcement Learning for General Video Game AI Deep Reinforcement Learning with Double Q-learning Playing Atari with Deep Reinforcement Learning Proximal Policy Optimization Algorithms Reinforcement Learning in Practice: Opportunities and Challenges State of the Art Control of Atari Games Using Shallow Reinforcement Learning
Game or genre specific solutions
[First Person Shooter] Playing FPS Games with Deep Reinforcement Learning ** Counter-Strike Deathmatch with Large-Scale Behavioural Cloning (Imitation learning but explains how to create data for games with no commercial APIs)
[MOBA] Learning Diverse Policies in MOBA Games via Macro-Goals Dynamic Difficulty Adjustment on MOBA Games Towards Playing Full MOBA Games with Deep Reinforcement Learning Mastering Complex Control in MOBA Games with Deep Reinforcement Learning Dota 2 with Large Scale Deep Reinforcement Learning
[Misc] Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning DanZero: Mastering GuanDan Game with Reinforcement Learning Deep RL Agent for a Real-Time Action Strategy Game Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario
Unsorted https://arxiv.org/pdf/2205.14953.pdf https://arxiv.org/pdf/2305.17352.pdf https://arxiv.org/pdf/2305.17886.pdf https://arxiv.org/pdf/2306.10715.pdf https://arxiv.org/ftp/arxiv/papers/2305/2305.09458.pdf https://arxiv.org/pdf/2303.15471.pdf https://arxiv.org/pdf/2103.01955.pdf https://arxiv.org/pdf/1707.06347.pdf https://arxiv.org/pdf/1907.11180.pdf
Review the existing collection of papers and identify additional relevant literature sources and collect papers