Reinforcement Learning with Gymnasium in Python
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
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Descripción del curso
Discover the World of Reinforcement Learning
Embark on an exhilarating exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. This interactive course takes you on a comprehensive journey through the core principles of RL where you'll master the art of training intelligent agents, teaching them to make strategic decisions and maximize rewards.Master Essential Concepts and Tools
Your adventure starts with a deep dive into the unique aspects of RL. You'll not only learn foundational RL concepts but also apply key RL algorithms to practical scenarios using the renowned OpenAI Gym toolkit. This hands-on approach ensures a thorough grasp of RL essentials.Navigate Through Advanced Strategies and Applications
As your journey unfolds, you'll venture into the realms of advanced RL strategies to discover the intricacies of Monte Carlo methods, Temporal Difference Learning, and Q-Learning. By mastering these techniques in Python, you'll be adept at training agents for a variety of complex tasks.Transform Your Learning into Real-World Impact
Concluding this course, you'll emerge with a profound understanding of RL theory, equipped with the skills to apply it creatively in real-world contexts. You'll be ready to build RL models in Python, unlocking a world of possibilities in your projects and professional endeavors.Empresas
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Introduction to Reinforcement Learning
GratuitoDive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.
Fundamentals of reinforcement learning50 xpWhat is Reinforcement Learning?50 xpRL vs. other ML sub-domains100 xpScenarios for applying RL100 xpNavigating the RL framework50 xpRL interaction loop100 xpEpisodic and continuous RL tasks100 xpCalculating discounted returns for agent strategies100 xpInteracting with Gymnasium environments50 xpSetting up a Mountain Car environment100 xpVisualizing the Mountain Car Environment100 xpInteracting with the Frozen Lake environment100 xp - 2
Model-Based Learning
Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.
Markov Decision Processes50 xpCustom Frozen Lake MDP components100 xpExploring state and action spaces100 xpTransition probabilities and rewards100 xpPolicies and state-value functions50 xpDefining a deterministic policy100 xpComputing state-values for a policy100 xpComparing policies100 xpAction-value functions50 xpComputing Q-values100 xpImproving a policy100 xpPolicy iteration and value iteration50 xpApplying policy iteration for optimal policy100 xpImplementing value iteration100 xp - 3
Model-Free Learning
Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.
Monte Carlo methods50 xpEpisode generation for Monte Carlo methods100 xpImplementing first-visit Monte Carlo100 xpImplementing every-visit Monte Carlo100 xpTemporal difference learning50 xpImplementing the SARSA update rule100 xpSolving 8x8 Frozen Lake with SARSA100 xpQ-learning50 xpImplementing Q-learning update rule100 xpSolving 8x8 Frozen Lake with Q-learning100 xpEvaluating policy on a slippery Frozen Lake100 xp - 4
Advanced Strategies in Model-Free RL
Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.
Expected SARSA50 xpExpected SARSA update rule100 xpApplying Expected SARSA100 xpDouble Q-learning50 xpImplementing double Q-learning update rule100 xpApplying double Q-learning100 xpBalancing exploration and exploitation50 xpDefining epsilon-greedy function100 xpSolving CliffWalking with epsilon greedy strategy100 xpSolving CliffWalking with decayed epsilon-greedy strategy100 xpMulti-armed bandits50 xpCreating a multi-armed bandit100 xpSolving a multi-armed bandit100 xpAssessing convergence in a multi-armed bandit100 xpCongratulations!50 xp
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