conftrace_

game ai

121 papers

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Co-occurring keywords

reinforcement learning (4352) large language model (13587) multi-agent system (1819) policy learning (702) deep reinforcement learning (903) monte carlo tree search (271) neural network (6616) game artificial intelligence (23) dialogue system (1667) benchmark evaluation (1556)

Papers

Real-time tree search with pessimistic scenarios: Winning the NeurIPS 2018 Pommerman Competition ACML 2019
Multi-Task Deep Reinforcement Learning with PopArt AAAI 2019
On Strength Adjustment for MCTS-Based Programs AAAI 2019
Strategic Tasks for Explainable Reinforcement Learning AAAI 2019
Playing Card-Based RTS Games with Deep Reinforcement Learning IJCAI 2019
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling ICML 2018
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract) IJCAI 2018
[RETRACTED] Playing 20 Question Game with Policy-Based Reinforcement Learning EMNLP 2018
High-Fidelity Simulated Players for Interactive Narrative Planning IJCAI 2018
Feedback-Based Tree Search for Reinforcement Learning ICML 2018
Algorithms or Actions? A Study in Large-Scale Reinforcement Learning IJCAI 2018
Three-Head Neural Network Architecture for Monte Carlo Tree Search IJCAI 2018
Balancing Two-Player Stochastic Games with Soft Q-Learning IJCAI 2018
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari IJCAI 2018
Interactive Learning and Decision Making: Foundations, Insights & Challenges IJCAI 2018
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games NIPS 2017
Real-Time Navigation in Classical Platform Games via Skill Reuse IJCAI 2017
Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well ICML 2016
Performance Bounds for λ Policy Iteration and Application to the Game of Tetris JMLR 2013
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris NIPS 2013
A Scalable Machine Learning Approach to Go NIPS 2006