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Learn Reinforcement Learning, earn certificates with paid and free online courses from Harvard, Stanford, MIT, Johns Hopkins and other top universities around the world. Read reviews to decide if a class is right for you.
Innovative approach to reinforcement learning using sequence modeling and Transformer architecture, offering simplicity and scalability while matching or exceeding state-of-the-art performance in various tasks.
Exploring safe Bayesian optimization and model-based deep reinforcement learning for efficient, safe online learning in real-world applications like robotics and laser tuning.
Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.
Explore sequential decision-making and reinforcement learning, covering utility theory, bandit problems, MDPs, POMDPs, Monte Carlo methods, and temporal difference learning. Gain practical skills through algorithms and examples.
Explores data augmentation techniques for image-based reinforcement learning, presenting a model-free algorithm and a self-supervised framework for visual continuous control tasks, achieving state-of-the-art results.
Explore adaptive learning systems and AI through Reinforcement Learning. Gain practical skills to implement RL solutions for real-world problems, from game development to industrial control.
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc.
Use Cutting-Edge Reinforcement Learning algorithms in Environments like Flappy Bird, Mario, Stocks and Much More!!
Explore reinforcement learning for trading strategies, covering neural networks, LSTMs, and AutoML. Learn to build RL-based trading systems and optimize portfolios using advanced machine learning techniques.
Develop Artificial Intelligence Applications using Reinforcement Learning in Python.
Explore Q-learning, deep Q networks, and reinforcement learning environments through hands-on tutorials and practical implementations.
Comprehensive exploration of reinforcement learning concepts, from basics to advanced topics, including deep RL, policy gradients, and practical applications in classic games.
Explore reinforcement learning for trading wind power futures, covering energy security, market setup, and machine learning models in this NYU seminar by Bruno Kamdem.
Explore reinforcement learning techniques for autonomous navigation in unmapped urban environments, focusing on value functions, courier tasks, and multi-site experiments.
Explore reinforcement learning techniques for urban navigation without maps, focusing on value functions, courier tasks, and multi-site experiments.
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