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Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football.

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Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control., , , , and . Sensors, 21 (23): 7829 (2021)Policy Generation from Latent Embeddings for Reinforcement Learning., , , and . ISPR (2), volume 1941 of Communications in Computer and Information Science, page 155-168. Springer, (2023)Causality Detection for Efficient Multi-Agent Reinforcement Learning., , and . AAMAS, page 2824-2826. ACM, (2023)Fully Independent Communication in Multi-Agent Reinforcement Learning., , , and . AAMAS, page 2423-2425. ACM, (2024)Towards Self-Adaptive Resilient Swarms Using Multi-Agent Reinforcement Learning., , and . ICPRAM, page 410-417. SCITEPRESS, (2024)Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football., , , and . CoRR, (2023)Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning., , , and . IEEE Trans. Neural Networks Learn. Syst., 35 (2): 1534-1544 (February 2024)Discovering Causality for Efficient Cooperation in Multi-Agent Environments., , and . CoRR, (2023)Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning., , , and . CoRR, (2022)Staged Reinforcement Learning for Complex Tasks Through Decomposed Environments., , , and . ISPR (2), volume 1941 of Communications in Computer and Information Science, page 141-154. Springer, (2023)