Competitive programming and machine learning may seem like separate domains, but there are fascinating areas of overlap to explore. One such area is using reinforcement learning (RL) techniques to automatically generate code that solves programming problems. While supervised learning approaches like OpenAI's Codex have shown impressive results in code generation, they rely on vast datasets of human-written programs. Reinforcement learning offers a compelling alternative — having an AI agent learn through trial-and-error interactions with a coding environment, guided by a reward signal. This mimics how humans learn new skills. A promising RL approach is policy gradient methods. Here the agent learns a policy — a probability distribution over different code generation actions to take given the current code state. The policy is parameterized by a neural network that takes in the partial code and outputs a probability for each next token to generate. By sampling code from this policy, getting a reward signal based on how well the code performs (e.g. how many test cases it passes), and updating the policy via stochastic gradient ascent, the policy progressively improves. Over many episodes, it learns patterns of code that solve the given problems more often. Applying this to competitive programming, we can train policies that solve classic problems like finding prime numbers, graph traversal, dynamic programming, etc. The states are the code written so far, actions are adding or editing lines of code, and rewards are based on code correctness and efficiency. The results are promising — RL-generated code can match or even sometimes surpass hand-coded solutions. And beyond just solving the problems, RL policies can discover novel and efficient implementations that humans may overlook. There are still challenges to overcome, like learning policies that generalize to new problems rather than just memorizing solutions. But RL offers an exciting path forward for AI systems that can autonomously write high-quality code. It also provides a valuable cross-pollination of ideas between the machine learning and competitive programming communities. I encourage researchers and competitive programmers to experiment with RL techniques like policy gradients. By open sourcing the environments and RL training code, we can accelerate innovation at the intersection of AI and programming challenges. This may lead us to powerful coding assistants and perhaps fundamentally new ways of writing software.