text: 241,5,723001334908100608,56872711,Tejas Kulkarni,"check our new paper on - Hierarchical Deep RL: Integrating temporal abstraction and intrinsic motivation"" <LINK> @iandanforth agreed re saliency. Ext rewards have always bothered me. But they seem appropriate to represent unalterable rewards w.r.t agent @Miles_Brundage if you chop top layer, model becomes DQN. Need flexible memory and qnet to learn objecty things to exploit top layer",https://arxiv.org/abs/1604.06057,"Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake rather than to directly solve problems. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse, delayed feedback: (1) a complex discrete stochastic decision process, and (2) the classic ATARI game `Montezuma's Revenge'. ","Hierarchical Deep Reinforcement Learning: Integrating Temporal