RL Fundamentals
Learn to train intelligent agents that actually converge
RL FOUNDATION
Types of Reinforcement Learning
1 Mathematical Foundations
1.1 Vectors
1.2 Derivatives
1.3 Gradients
1.4 Spaces
1.5 Normalization
1.6 Function Approximation
2 Core RL Concepts
2.1 Problem Classification
2.2 Bellman Equation
2.3 Model Free Learning
2.4 Reward Shaping
2.5 On-Policy vs Off-Policy Learning
2.6 Agent
2.7 Markov Decision Process(MDP)
3 Learning Strategies
3.1 Choosing RL Algorithm
3.2 Epsilon-greedy
3.3 SIM2REAL
3.4 Experience Replay
3.5 Curriculum Learning
3.6 Isaac Sim
4 Deep RL Techniques
4.1 Backpropagation
4.2 Weight Initialization
4.3 Gradient Descent
4.4 ReLU Activation Function
4.5 Artificial Neuron
4.6 Adam Optimization
4.7 Convolutional Neural Network
5 RL Algorithms
Q-Learning
Deep Q Network (DQN) – Formula and Explanation
Double DQN
Dueling DQN
Proximal Policy Optimization (PPO)
Soft Actor-Critic (SAC)
CLASSIC DEEP RL APPLICATION
PART 1: Deep RL with DQN and CNN
PART 2: Problem Definition
PART 3: Markov Decision Process (MDP)
PART 4: Choosing the Algorithm
PART 5: Environment + RL Model + Reward Function
PART 6: Training + Testing + Google Colab Access
Q-Learning
Deep RL Algorithms
DQN
PPO
SAC
Simulation & Environments
OpenAI Gymnasium
Tools, Code & Experiment Design
PyTorch
Stable-Baselines3
No Result
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RL Fundamentals
Learn to train intelligent agents that actually converge
RL FOUNDATION
Types of Reinforcement Learning
1 Mathematical Foundations
1.1 Vectors
1.2 Derivatives
1.3 Gradients
1.4 Spaces
1.5 Normalization
1.6 Function Approximation
2 Core RL Concepts
2.1 Problem Classification
2.2 Bellman Equation
2.3 Model Free Learning
2.4 Reward Shaping
2.5 On-Policy vs Off-Policy Learning
2.6 Agent
2.7 Markov Decision Process(MDP)
3 Learning Strategies
3.1 Choosing RL Algorithm
3.2 Epsilon-greedy
3.3 SIM2REAL
3.4 Experience Replay
3.5 Curriculum Learning
3.6 Isaac Sim
4 Deep RL Techniques
4.1 Backpropagation
4.2 Weight Initialization
4.3 Gradient Descent
4.4 ReLU Activation Function
4.5 Artificial Neuron
4.6 Adam Optimization
4.7 Convolutional Neural Network
5 RL Algorithms
Q-Learning
Deep Q Network (DQN) – Formula and Explanation
Double DQN
Dueling DQN
Proximal Policy Optimization (PPO)
Soft Actor-Critic (SAC)
CLASSIC DEEP RL APPLICATION
PART 1: Deep RL with DQN and CNN
PART 2: Problem Definition
PART 3: Markov Decision Process (MDP)
PART 4: Choosing the Algorithm
PART 5: Environment + RL Model + Reward Function
PART 6: Training + Testing + Google Colab Access
Q-Learning
Deep RL Algorithms
DQN
PPO
SAC
Simulation & Environments
OpenAI Gymnasium
Tools, Code & Experiment Design
PyTorch
Stable-Baselines3
No Result
View All Result
No Result
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MuJoCo
How To Setup MuJoCo, Gymnasium, PyTorch, SB3 and TensorBoard on Windows
March 4, 2026
MuJoCo
A detailed look at MuJoCo environments for RL, including custom robotics tasks, physics tuning, and training pipelines for continuous-control agents.
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RL Fundamentals
Learn to train intelligent agents that actually converge
RL FOUNDATION
CLASSIC DEEP RL APPLICATION
Q-Learning
Deep RL Algorithms
DQN
PPO
SAC
Simulation & Environments
OpenAI Gymnasium
Tools, Code & Experiment Design
PyTorch
Stable-Baselines3
© 2026 Reinforcement Learning Path
