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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Principled Path to Fitted Distributional Evaluation
Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
Meta-learning how to Share Credit among Macro-Actions
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon
NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War
TextAtari: 100K Frames Game Playing with Language Agents
Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation
Action-Dependent Optimality-Preserving Reward Shaping
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning
PoE-World: Compositional World Modeling with Products of Programmatic Experts
Automatic Reward Shaping from Confounded Offline Data
Unraveling the Rainbow: can value-based methods schedule?
Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents
Pay Attention to What and Where? Interpretable Feature Extractor in Vision-based Deep Reinforcement Learning
Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata
Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning
Adventurer: Exploration with BiGAN for Deep Reinforcement Learning
Agents Play Thousands of 3D Video Games
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games
AVA: Attentive VLM Agent for Mastering StarCraft II
Target Return Optimizer for Multi-Game Decision Transformer
Trajectory-Class-Aware Multi-Agent Reinforcement Learning
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning
OptionZero: Planning with Learned Options
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
Learning To Explore With Predictive World Model Via Self-Supervised Learning
Uncovering Untapped Potential in Sample-Efficient World Model Agents
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations
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