Bootstrapping in reinforcement learning refers to using an estimate of the value of a state or action to update the value of other states or actions. Rather than waiting for the final reward to complete a sequence, bootstrapping allows the agent to update its estimates incrementally using its current knowledge.
For example, in Temporal Difference (TD) learning, the agent updates its Q-values using the current value estimate for the next state rather than waiting for the final reward. This allows the agent to improve its policy faster by using partial information instead of waiting for the entire episode to finish.
Bootstrapping is an essential technique in many RL algorithms, as it speeds up the learning process and helps the agent adapt to the environment more efficiently