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REINFORCEjs is a JavaScript reinforcement-learning library built to demonstrate several major RL algorithms directly in the browser. It includes dynamic programming methods for finite environments and tabular temporal-difference learning with SARSA and Q-learning. A Deep Q-Learning agent extends Q-learning to continuous state features through neural-network function approximation. The project also experiments with stochastic and deterministic policy gradients and actor-critic methods for continuous action spaces. ...