Reinforcement Learning (RL)
a high-level framework for solving sequential decision-making problems. An RL agent navigates an environment by taking actions based on some observations, receiving rewards as a result. Most RL algorithms work by maximizing the expected total rewards an agent collects in a trajectory, e.g., during one in-game round. - Deep Reinforcement Learning With TensorFlow 2.1
see also
- Apprentissage par renforcement #2 - Exploration et Exploitation
- Value Function - backtracking from final state
- Premier Jeu - jeux des allumettes - value fonction simple a apprendre
- Q-Learning - equation de bellman - definition recursive de la q-function
- Deep Q-Learning
- If you want to try… - from Mastering a Pod Racing AI Competition with Nash-DQN
Written on January 11, 2025, Last update on July 4, 2025
RL
NN