Comparative Analysis of Bandit Algorithms for Optimal Decision-Making
Explore a comprehensive comparative analysis of various bandit algorithms used in reinforcement learning for optimal decision-making. This video showcases the implementation and evaluation of different methods such as Greedy, Epsilon-Greedy, UCB, and more, highlighting their strengths and performance in selecting optimal actions. Gain insights into the trade-off between exploration and exploitation strategies and learn how these algorithms can enhance decision-making systems. Join us for a deep dive into the world of bandit algorithms and their applications.
YouTube: https://youtu.be/K2dPVza-pSQ
🆔 @MATLAB_House
@MATLABHOUSE
#ReinforcementLearning #BanditAlgorithms #DecisionMaking #ExplorationVsExploitation #OptimalActionSelection #MachineLearning #DataScience #AI #CodeImplementation #AlgorithmComparison #PerformanceAnalysis
Explore a comprehensive comparative analysis of various bandit algorithms used in reinforcement learning for optimal decision-making. This video showcases the implementation and evaluation of different methods such as Greedy, Epsilon-Greedy, UCB, and more, highlighting their strengths and performance in selecting optimal actions. Gain insights into the trade-off between exploration and exploitation strategies and learn how these algorithms can enhance decision-making systems. Join us for a deep dive into the world of bandit algorithms and their applications.
YouTube: https://youtu.be/K2dPVza-pSQ
🆔 @MATLAB_House
@MATLABHOUSE
#ReinforcementLearning #BanditAlgorithms #DecisionMaking #ExplorationVsExploitation #OptimalActionSelection #MachineLearning #DataScience #AI #CodeImplementation #AlgorithmComparison #PerformanceAnalysis
Reinforcement Learning in Gridworld: Solving the Windy Grid Problem
Watch this video showcasing the implementation of a reinforcement learning algorithm in solving the Windy Grid Problem. The algorithm uses Q-learning with epsilon-greedy exploration to navigate a gridworld with varying wind powers. Learn how the agent learns to reach the goal by optimizing its actions based on rewards and Q-values. The video includes visualizations of the grid, wind powers, and the agent's path.
YouTube: https://youtu.be/AiI_4flFmYc
🆔 @MATLAB_House
@MATLABHOUSE
#ReinforcementLearning #Qlearning #Gridworld #WindyGridProblem #ArtificialIntelligence #MachineLearning #CodingTutorial #Python #Algorithm #AI
Watch this video showcasing the implementation of a reinforcement learning algorithm in solving the Windy Grid Problem. The algorithm uses Q-learning with epsilon-greedy exploration to navigate a gridworld with varying wind powers. Learn how the agent learns to reach the goal by optimizing its actions based on rewards and Q-values. The video includes visualizations of the grid, wind powers, and the agent's path.
YouTube: https://youtu.be/AiI_4flFmYc
🆔 @MATLAB_House
@MATLABHOUSE
#ReinforcementLearning #Qlearning #Gridworld #WindyGridProblem #ArtificialIntelligence #MachineLearning #CodingTutorial #Python #Algorithm #AI
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✳️Inverted Pendulum Control: RL + MPC Implementation
1️⃣ Reinforcement Learning Features:
- Q-Learning for system identification
- Self-learning pendulum balancing
- No prior model needed
2️⃣ MPC Implementation:
- Real-time optimization
- Constraint handling
- Precise position/angle control
3️⃣ Hardware:
- DC motor (50:1 gearbox)
- Dual encoders
- STM32 controller
- Custom PWM driver
4️⃣ Performance:
- Upright stabilization
- Disturbance rejection
- Accurate tracking
By: Javad Safaei
Supervisor: Naser Pakar
🔹Telegram:
🆔 @MATLAB_House
@MATLABHOUSE
#InvertedPendulum #ReinforcementLearning #ModelPredictiveControl #Robotics #ControlSystems #Engineering
1️⃣ Reinforcement Learning Features:
- Q-Learning for system identification
- Self-learning pendulum balancing
- No prior model needed
2️⃣ MPC Implementation:
- Real-time optimization
- Constraint handling
- Precise position/angle control
3️⃣ Hardware:
- DC motor (50:1 gearbox)
- Dual encoders
- STM32 controller
- Custom PWM driver
4️⃣ Performance:
- Upright stabilization
- Disturbance rejection
- Accurate tracking
By: Javad Safaei
Supervisor: Naser Pakar
🔹Telegram:
🆔 @MATLAB_House
@MATLABHOUSE
#InvertedPendulum #ReinforcementLearning #ModelPredictiveControl #Robotics #ControlSystems #Engineering
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