In this example we implement a simple Deep Reinforcement Learning solution for the classic CartPole problem. We implement a custom reward function and then we use three different strategies to strike a balance between exploration and exploitation and use DQN to approximate Q-values: - Epsilon greedy exploration - Noisy Networks - Thompson Sampling