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Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations Reinforcement Learning (RL) is concerned with enabling agents, i.e. decision makers, to learn how to accomplish a task by taking sequential actions in a given stochastic environment and relies on Markov Decision Processes (MDP). In recent years, Deep Reinforcement Learning (DRL), which leverages RL approaches using Deep Neural Network based function approximators, has been proved to achieve human-level performance in a number of applications requiring an individual agent, mostly gaming environments. (Continued...)