Policy Gradient Methods Reinforcement Learning Delving into the realm of reinforcement learning, policy gradient methods stand out as a strategy that directly tweaks the policy, mapping states to actions, to enhance performance. Unlike methods that estimate value functions, policy gradient methods adjust the policy parameters (θ) by ascending along the gradient of the expected reward. This approach can be likened to climbing a mountain, feeling the slope underfoot and taking steps upwards, always aiming for the peak where the reward is maximized. Unveiling Policy Optimization At the core of policy gradient methods lies policy optimization, aiming