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Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 3525 posts
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[Deep RL] 20. Deep RL Summary: Algorithm Comparison and Selection Guide
Systematic comparison and selection criteria for deep RL algorithms: value-based, policy-based, Actor-Critic, and model-based approaches
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 17. Model-Based Reinforcement Learning: Imagination-Augmented Agent
Principles of model-based RL and I2A (Imagination-Augmented Agent): learning environment models and planning through imagination
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 14. Continuous Action Spaces: DDPG and Distributional Policies
Methods for handling continuous action spaces: A2C extension, DDPG deterministic policy gradient, and distributional policy gradient
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai[Deep RL] 11. A3C: Asynchronous Advantage Actor-Critic
How A3C solves the correlation problem of A2C through asynchronous parallel learning: data parallelism and gradient parallelism implementation
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai[Deep RL] 08. Building a Stock Trading Agent with Reinforcement Learning ♪ Listenable
Define stock trading as a reinforcement learning problem, design a custom trading environment, and train trading agents using feedforward and CNN models.
2026-03-19 · 19 min read #reinforcement-learning#deep-learning#ai#finance[Deep RL] 05. Bellman Equation and Value Iteration
Understand the concepts of state value and action value, and apply value iteration and Q-learning to FrozenLake based on the Bellman optimality equation.
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 02. Getting Started with Reinforcement Learning Using OpenAI Gym
Understand the structure and API of OpenAI Gym, and practice with random agents and wrappers in the CartPole environment.
2026-03-19 · 10 min read #reinforcement-learning#deep-learning#ai[Deep RL] 18. AlphaGo Zero: AI That Learns by Playing Itself
Core principles of AlphaGo Zero: MCTS and self-play, plus implementing a Connect4 bot
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 15. Trust Region Methods: TRPO, PPO, ACKTR
Trust region techniques for stable policy updates: PPO clipping objective, TRPO constrained optimization, and ACKTR
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 12. Training Chatbots with Reinforcement Learning
Combining Seq2Seq models with reinforcement learning for chatbot training: from RNN basics to Self-Critical Sequence Training (SCST)
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai#nlp[Deep RL] 09. Policy Gradient: Policy-Based Reinforcement Learning
Understand the difference between value-based and policy-based methods, and apply the mathematical derivation of Policy Gradient and the REINFORCE algorithm to CartPole and Pong.
2026-03-19 · 6 min read #reinforcement-learning#deep-learning#ai#policy-gradient[Deep RL] 06. Deep Q-Network: Principles and Implementation of DQN
Understand the core ideas of DQN (experience replay, target network) that overcome the limitations of table-based Q-learning, and implement it in the Pong environment.
2026-03-19 · 10 min read #reinforcement-learning#deep-learning#ai#dqn[Deep RL] 03. PyTorch Deep Learning Basics: From Tensors to Neural Networks ♪ Listenable
Learn PyTorch tensor operations, automatic differentiation, and neural network components, then implement TensorBoard monitoring and a GAN example using Atari images.
2026-03-19 · 20 min read #reinforcement-learning#deep-learning#ai#pytorch[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#ai