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These are javascript implementations of Arthur Juliani's Simple Reinforcement Learning with Tensorflow series, built using Tensorflow.js.

Part 0 — Q-Learning Agents

Part 1 — Two-Armed Bandit

Part 1.5 — Contextual Bandits

Part 2 — Policy-Based Agents

Part 3 — Model-Based RL

Part 4 — Deep Q-Networks and Beyond

Part 5 — Visualizing an Agent’s Thoughts and Actions

Part 6 — Partial Observability and Deep Recurrent Q-Networks

Part 7 — Action-Selection Strategies for Exploration

Part 8 — Asynchronous Actor-Critic Agents (A3C)