TD3 is an advanced algorithm for reinforcement learning that builds on the DDPG algorithm. It aims to address overestimation bias with the value function, which is a common problem in reinforcement learning. The TD3 algorithm uses three key modifications: clipped double Q-learning, delayed update of target and policy networks, and target policy smoothing.
What is reinforcement learning?
Reinforcement learning is a type of machine learning that involves an agent learning to make decisions based on feedback from its environment. The agent takes actions in the environment and receives rewards or punishments based on its actions. The goal of reinforcement learning is to find an optimal policy, or a sequence of actions, that maximizes the cumulative reward over time.
What is the DDPG algorithm?
The DDPG (Deep Deterministic Policy Gradient) algorithm is a deep reinforcement learning algorithm that is used for continuous control tasks. It combines ideas from deep learning and traditional reinforcement learning to learn a deterministic policy directly from high-dimensional sensory input.
How does TD3 improve on DDPG?
TD3 (Twin Delayed DDPG) builds on the DDPG algorithm by using three key modifications aimed at improving the accuracy of the value function and reducing overestimation bias:
Clipped double Q-learning
The first modification that TD3 uses is clipped double Q-learning. This involves using two separate value functions, called Q1 and Q2, to estimate the value of each state-action pair. Rather than simply taking the maximum value of Q1 and Q2, TD3 takes the minimum value to avoid overestimating the value function.
Delayed update of target and policy networks
The second modification involves delaying the updates of the target and policy networks. This means that the TD3 algorithm only updates the target and policy networks every n steps, rather than every step. This helps to prevent the algorithm from overfitting to recent experiences and improves the stability of the learning process.
Target policy smoothing
The third modification is target policy smoothing, which is similar to a SARSA-based update. This involves adding noise to the target policy during training to make it more robust to perturbations. This helps to reduce overestimation bias and improve convergence.
Why is reducing overestimation bias important?
Overestimation bias occurs when the value function overestimates the true value of a state-action pair. This can lead to the agent taking suboptimal actions, which can result in slower learning and suboptimal policies. Reducing overestimation bias is therefore an important goal of reinforcement learning algorithms.
Overall, TD3 is an advanced reinforcement learning algorithm that builds on the DDPG algorithm by using three key modifications aimed at reducing overestimation bias and improving the stability of the learning process. These modifications include clipped double Q-learning, delayed update of target and policy networks, and target policy smoothing. By reducing overestimation bias, the TD3 algorithm is better equipped to learn optimal policies for continuous control tasks.
