In the context of policy gradients, 'reward-to-go' represents the accumulated rewards from a specific state until the end of an episode. It is an important quantity for estimating the value function. The value function refers to the expected reward that an agent can achieve starting from a given state and following a certain policy. By considering the reward-to-go for all states, we can gain insights into the expected rewards for different states and use this information to optimize the policy.

When we talk about 'reward-to-go' in policy gradients, we are essentially referring to the sum of rewards obtained from a particular state until the end of an episode. This concept allows us to estimate the value function, which represents the expected cumulative reward an agent can achieve from a given state onwards, following a specific policy. By calculating the reward-to-go for all states, we can gain a comprehensive understanding of the value function, which is crucial in optimizing the policy for reinforcement learning tasks.

Reward-to-go refers to the concept of calculating the cumulative reward received from a particular state to the end of an episode. It is commonly used in policy gradient methods to estimate the expected return or value function. By summing up the rewards obtained starting from a specific state until the end, we can approximate the value function. This allows us to evaluate and update the policy based on the expected rewards for different states.