Trajectory Optimization

Modifying reward to accelerate learning.

Reward only given upon task completion.

Learning policies from expert demonstrations.

Learning action mapping directly from demonstrations.

RL without explicit dynamics model.

RL using learned or known environment models.

Predicts next state given current state and action.

Directly optimizing control policies.