reinforcement learning methods

We have hosted the application reinforcement learning methods in order to run this application in our online workstations with Wine or directly.

Quick description about reinforcement learning methods:

Reinforcement-Learning-with-TensorFlow is an educational repository that walks through key reinforcement learning algorithms implemented in TensorFlow. It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can easily trace how actions, rewards, and model updates connect. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. By using TensorFlow as the backbone, it highlights practical considerations such as tensor shapes, loss computation, optimization steps, and batching in an RL context.

Features:

• Educational RL implementations using TensorFlow
• Q-learning and Deep Q-Network examples
• Policy gradient and actor-critic algorithm scripts
• Simulation environment integrations for interactive learning
• Visualizations of learning curves and policy behavior
• Readable, beginner-friendly code with explanations

Programming Language: Python.
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