EE5521: Optimal control
EE5531: Reinforcement learning based control (interactive demos)
- Q-learning on a five-tile corridor
Step through choose, move, reward and update, and watch the Q-table fill in.
- DQN v0.1: information flow
Follow one transition through the Q-network, the policy, the loss and the weight update.
- DQN v0.2: experience replay
See transitions stored in a replay buffer and learned from in random mini-batches.
- DQN v1.0: experience replay and fixed targets
Add a frozen target network, copied from the evaluation network every k steps.
- DDPG: how the pieces fit together
Actor, critic, target copies, OU exploration noise and soft updates, one time step at a time.
- DDPG training demos