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Data Party THU
Data Party THU
Nov 23, 2025 · Artificial Intelligence

Can a Drone Learn to Land Itself? A Deep Reinforcement Learning Walkthrough

This article walks through the fundamentals of reinforcement learning, builds a custom drone‑landing simulation, defines state and action spaces, designs reward functions, implements a neural‑network policy with Bernoulli sampling, and trains it using REINFORCE with baseline techniques, while exposing common pitfalls such as reward‑cheating.

OpenAI GymPolicy GradientPython
0 likes · 22 min read
Can a Drone Learn to Land Itself? A Deep Reinforcement Learning Walkthrough
Code DAO
Code DAO
Dec 3, 2021 · Artificial Intelligence

Understanding Actor‑Critic and A2C: From Policy Gradients to REINFORCE in RL

This article derives the policy‑gradient objective for discrete actions, implements the Monte‑Carlo REINFORCE algorithm in PyTorch, explains the actor‑critic framework, introduces Advantage Actor‑Critic (A2C) versus A3C, and demonstrates their performance on the OpenAI Gym CartPole‑v0 environment.

A2COpenAI GymPolicy Gradient
0 likes · 13 min read
Understanding Actor‑Critic and A2C: From Policy Gradients to REINFORCE in RL
Code DAO
Code DAO
Nov 28, 2021 · Artificial Intelligence

Adapting Soft Actor‑Critic for Discrete Action Spaces in Deep Reinforcement Learning

This article explains how to modify the Soft Actor‑Critic (SAC) algorithm—originally designed for continuous actions—to work with discrete action environments, presents the required changes to the actor and critic loss functions, provides a full PyTorch implementation, and evaluates the method on the CartPole‑v1 benchmark.

CartPoleDiscrete ActionsEntropy Regularization
0 likes · 20 min read
Adapting Soft Actor‑Critic for Discrete Action Spaces in Deep Reinforcement Learning
DataFunTalk
DataFunTalk
Nov 12, 2020 · Artificial Intelligence

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

This article explains how reinforcement learning, with its focus on maximizing long‑term reward, can improve recommendation system mixing by covering basic RL concepts, differences from supervised learning, multi‑armed bandit approaches, practical OpenAI Gym experiments, new AUC metrics, online gains, and advanced model optimizations.

Artificial IntelligenceOpenAI GymQ-Learning
0 likes · 10 min read
Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation