Imagination in an agent specifies visualizing and planning before taking any action.
Imagination core consists of policy network and environmental model for performing imagination.
Agents repeatedly take feedback from the human and change its goal according to the human preference.
DQfd uses some demonstration data for training where as DQN doesn't use any demonstrations data upfront.
Refer section Hindsight Experience Replay (HER).
Hierarchical reinforcement learning (HRL) is proposed to solve the curse of dimensionality where we decompress large problems into small subproblems in a hierarchy
We tried to find the optimal policy given the reward function in RL whereas in inverse reinforcement learning, the optimal policy is given and we find the reward function
