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States, Actions, Rewards - RL Fundamentals | OneNoughtOne

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Coordinate Transformation Between Bases
Coordinate Transformation Between Bases
Closed-Form Linear Model Fitting
Column Compressed Sparse Matrix Format
Component-wise Vector Addition
Linear Kernel Computation (Dot Product Similarity)
Linear Transformation of a Vector
Square Matrix Determinant and Trace Evaluator
Matrix Dimension Restructuring
Computing the Multiplicative Inverse of a 2×2 Matrix
Matrix Row-Column Interchange
Scaling a Matrix by a Constant Factor
Polynomial Kernel Similarity Computation
Hyperplane Separation Width for Linear Classifiers
Three-Dimensional Vector Cross Product
Vector Inner Product Computation
Vector Projection onto a Line
Construct Diagonal Matrix from Vector
Column Space Basis Extraction
Doubly Stochastic Stream Mixing Forward Pass
Gauss-Seidel Linear System Solver
Gaussian Kernel Similarity Matrix
Hyperdimensional Row Encoding Using Bind-Bundle Operations
Low-Rank Adaptation Forward Computation
Pairwise Matrix Product Computation
Surprise-Driven Neural Memory Update with Momentum
Two-Dimensional Orthonormal Basis Generator
Row Reduced Canonical Form
Similarity Matrix Transformation
Spatial Translation Transformation in 2D
Spectral Analysis of a Square Matrix

1.Agent-Environment Interaction 2.States, Actions, Rewards 3.Policies 4.Value Functions 5.Markov Property

Cumulative Reward with Temporal Decay
Cumulative Reward with Temporal Decay
Exploration Bonus Action Selector
Intra-Group Advantage Normalization for Policy Training
Unbiased Policy Divergence Estimation for Regularized Optimization
Sample Accuracy and Consensus Voting Metrics
Streaming Average Update for Real-Time Estimation
Bellman Prediction Error for State Value Updates
Trajectory Cumulative Discounted Reward
Adversarial Critic Reward Computation
Token-Budget Aware Reinforcement Learning Loss
Exploration-Exploitation Action Chooser
Grid Navigation Value Estimation
Initial Encounter Value Estimation from Episodic Experience
Action Value Estimation in Markov Decision Processes
Multi-Step Value Estimation
On-Policy Temporal Difference Learning for Action-Value Estimation
Preference-Based Action Selector with Softmax Probabilities
Recency Weighted Value Estimate
State-Value Function Bellman Update
Temporal Difference Value Estimator for Sequential Decision Problems
Unbiased Policy Gradient Objective Computation
Coin Flip Wealth Strategy Optimizer
Group Policy Optimization Objective
Policy Gradient Computation via REINFORCE
Sequence-Level Policy Gradient Objective
Simulation-Based Game Tree Exploration
TD(λ) Value Estimation with Eligibility Traces

Course overview & roadmap

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States, Actions, Rewards

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