Deep Deterministic Policy Gradient (DDPG)
Implementation of this algorithm can be found in the DDPG_Demo.ipynb notebook.
Deep Deterministic Policy Gradient (DDPG) is a model-free reinforcement learning algorithm designed for continuous action spaces. It combines the actor-critic framework with deterministic policy gradients. Key features of DDPG include:
- Actor-Critic Architecture: Utilizes an actor network to select actions and a critic network to evaluate the Q-values of state-action pairs.
- Off-Policy Learning: Learns using a replay buffer to sample past experiences, improving stability and efficiency.
- Target Networks: Employs slowly updated target networks to stabilize training.
- Exploration: Uses noise (e.g., Ornstein-Uhlenbeck process) for exploration in continuous spaces.