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11 articles
Explores how the ViZDoom platform drives advancements in embodied AI, detailing the evolution from standard Deep Q-Networks to hierarchical architectures capable of mastering complex, 3D environments
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Exploring training a drone with reinforcement learning, focusing on the trade-offs between sparse and dense rewards to achieve agile flight while avoiding unintended "reward hacking."
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A complete guide to the AutoDRIVE ecosystem. Learn to train self-driving car agents from scratch using reinforcement learning in this powerful Unity-based simulator.
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A walkthrough of reinforcement learning for stock trading with FinRL: the MDP and Bellman theory underneath it, building a trading environment, and training and backtesting agents with Stable-Baselines3.
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Using reinforcement learning (RL), specifically Truncated Quantile Critics (TQC) and Deep Deterministic Policy Gradient (DDPG), to solve the Fetch environments in Gymnasium Robotics
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A beginner's walkthrough of Model-Based Reinforcement Learning on Atari's Breakout: training an action-conditional world model that predicts frames, rewards and terminals, then learning a PPO policy entirely inside it with SimPLe
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Discover how self-play can be used to train a reinforcement learning agent to master Connect 4, achieving advanced strategies without human intervention
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Train an RL agent to master Atari's Pong with sparse rewards and high-dimensional inputs. Explore preprocessing, replay buffers, and performance-boosting strategies
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Learn how to apply reinforcement learning to solve Gymnasium's Car Racing game, see how different algorithms perform, and explore whether discrete or continuous action spaces are better.
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Explore how different On-Policy and Off-Policy reinforcement learning algorithms perform on Gymnasium's Lunar Lander
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Découvrez le fonctionnement de l'algorithme d'apprentissage par renforcement Deep Q-Learning (DQN) et appliquez-le pour résoudre Lunar Lander de Gymnasium.
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