Reinforcement Learning Algorithms with Python

Deterministic policy gradient

Designing an algorithm that is both off-policy and able to learn stable policies in high-dimensional action spaces is challenging. DQN already solves the problem of learning a stable deep neural network policy in off-policy settings. An approach to making DQN also suitable for continuous actions is to discretize the action space. For example, if an action has values between 0 and 1, a solution could be to discretize it in 11 values (0, 0.1, 0.2,.., 0.9, 1.0), and predict their probabilities using DQN. However, this solution is not manageable with a lot of actions, because the number of possible discrete actions increases exponentially with the degree of freedom of the agent. Moreover, this technique isn't applicable ...