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Learn Reinforcement Learning, earn certificates with paid and free online courses from Harvard, Stanford, MIT, University of Michigan 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.
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Master the fundamentals of reinforcement learning (RL) to create models that can navigate complex real-world environments and train LLMs.
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.
Implement Cutting Edge Artificial Intelligence Research Papers in the Open AI Gym Using the PyTorch & Tensorflow2
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.
All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: DDPG, TD3, SAC, NAF, HER.
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