Implementing REINFORCE Policy Gradient Algorithm from Scratch with NumPy
This article demonstrates how to implement the REINFORCE policy gradient algorithm entirely from scratch using NumPy, without relying on deep learning frameworks. The author trains the algorithm to balance the CartPole environment, showing its convergence to the maximum score.
Why it matters
This article provides a valuable hands-on example of implementing a fundamental reinforcement learning algorithm from scratch, which can help developers gain a deeper understanding of policy gradient methods.
Key Points
- 1Policy gradient methods directly parameterize the policy and optimize it via gradient ascent
- 2The REINFORCE algorithm is implemented from scratch, including the forward pass, backpropagation, and RMSProp optimizer
- 3The algorithm is trained to balance the CartPole environment, converging to the maximum score of 500 within about 3,000 episodes
Details
The article explains that policy gradient methods, such as REINFORCE, directly parameterize the policy and optimize it via gradient ascent, rather than learning a value function and deriving a policy. This approach is useful for continuous action spaces where argmax cannot be applied. The author implements the REINFORCE algorithm entirely from scratch using NumPy, including the forward pass, backpropagation, and RMSProp optimizer. The implementation is demonstrated on the CartPole environment, where the agent learns to balance the pole and converges to the maximum score of 500 within about 3,000 episodes. This showcases the power of policy gradient methods and the ability to implement them from scratch without relying on deep learning frameworks.
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