Tag: #reinforcement-learning
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 29 posts
[Deep RL] 19. Practical Applications of Deep Reinforcement Learning
Diverse real-world applications of deep RL: robot control, autonomous driving, resource management, recommendation systems, NLP, and game AI
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai[Deep RL] 16. Black-Box Optimization: Evolution Strategies and Genetic Algorithms
Optimizing policies without gradients: principles and implementation of Evolution Strategies (ES) and Genetic Algorithms (GA)
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 13. Web Navigation and Reinforcement Learning
Combining browser automation with reinforcement learning: implementing web agents using the Mini World of Bits benchmark and OpenAI Universe
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 10. Actor-Critic Methods: A2C and Hyperparameter Tuning
Understand the Actor-Critic architecture that solves the variance problem of REINFORCE, apply the A2C algorithm to Pong, and analyze the impact of key hyperparameters such as learning rate and entropy coefficient.
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai#actor-critic[Deep RL] 07. DQN Extensions: Double DQN, Dueling DQN, Rainbow
Implement six core extension techniques that improve DQN performance (N-step, Double DQN, Noisy Networks, Prioritized Replay, Dueling DQN, Categorical DQN) and Rainbow which combines them all.
2026-03-19 · 11 min read #reinforcement-learning#deep-learning#ai#dqn[Deep RL] 04. Solving CartPole with the Cross-Entropy Method
Explore the taxonomy of reinforcement learning methods and solve CartPole and FrozenLake environments using the Cross-Entropy method.
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 01. What is Reinforcement Learning: MDP and Fundamental Concepts
We compare three paradigms of machine learning, and explore the core components of reinforcement learning (agent, environment, reward) along with the mathematical foundations of Markov Decision Processes (MDP).
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#aiReinforcement Learning Complete Guide: From DQN, PPO to RLHF and DPO for LLM Alignment
From MDP fundamentals to DQN, PPO, SAC, RLHF, and DPO. Master the reinforcement learning techniques used for LLM alignment with PyTorch and Stable-Baselines3 code.
2026-03-17 · 14 min read #reinforcement-learning#ppo#dqn#rlhf#dpoReinforcement Learning Complete Guide: From Theory to the Latest Algorithms and Real Implementations ♪ Listenable
Derives the major algorithms with their mathematics — from the core theory of MDPs and the Bellman equations through Q-Learning, DQN, Policy Gradient, A3C, PPO and SAC — and pulls together RLHF, AlphaGo and robotics appl
2026-03-01 · 46 min read #reinforcement-learning#deep-learning#dqn#ppo#rlhf