Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Exploration in environments with sparse rewards is a key challenge for reinforcement learning.
How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy?
We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well.
We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning.
These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards.
We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.
Exploration in environments with sparse feedback is a key challenge for deep reinforcement learning (DRL) research.
In DRL, agents typically explore with local exploration strategies like epsilon greedy or entropy based schemes.
We are interested in learning structured exploration algorithms, grounded in spatio-temporal visual abstractions given raw pixels.
In human perception and its developmental trajectory, spatio-temporal pixel groupings is one of the first visual abstractions to emerge, which is also used for intrinsically motivated goal-driven behaviors BID16 .
Inspired by this insight, we develop a new agent architecture and loss functions to autonomously learn visual abstractions and ground temporally extended behaviors in them.Our approach and key contributions can be broken down into two parts: (1) an information theoretic loss function and a neural network architecture to learn visual groupings (abstractions) given raw pixels and actions, (2) a hierarchical action-value function agent which explores in the space of options grounded in the learned visual abstractions, instead of low level actions.In the first step, we pass images through an encoder which outputs spatial discrete vector-quantized (VQ') grids, with 1 of E discrete components.
We train this encoder to maximize the mutual information between VQ layers at different time steps, in order to obtain a temporally consistent representation, that preserves controllability and appearance information.
We extract segmentation masks from the VQ layers for the second step, referred to as visual entities.
We compute affine geometric measurements for each entity, namely centroid and area of the corresponding segment.
We use off-policy learning to train action-value function to minimize or maximize these measurements, referred collectively as the options bank.
Controlling these measurements enable higher levels of behaviors such as approaching an object (maximizing area), avoiding objects (minimize area or minimize/maximize centroid coordinates), moving an object away towards the left (minimize centroid x coordinate), controlling the avatars position on the screen etc.
Finally, given a task reward, we use off-policy learning to train a meta action-value function that takes actions at fixed intervals and selects either one of the policies in the options bank or low-level actions.
So effectively, this hierarchical action-value function setup solves a semi markov decision process as in BID18 BID9 .We
demonstrate that our approach can scale to two different domains -navigation in a 3D environment and challenging Atari games -given raw pixels. Although
much work remains in improving the visual and temporal abstraction discovery models, our results indicate that it is possible to learn bottom-up structured exploration schemes with simple spatial inductive biases and loss functions.
We have shown that it is possible to design unsupervised structured exploration schemes for modelfree DRL agents, with competitive performance on a range of environments given just raw pixels.One of the biggest open question moving forward is to find strategies to balance structure or inductive biases and performance.
Our current solution was to augment the meta-controller with Q task along with the options bank as sub-behaviors.
The typical strategy that agents follow is to rely on the options bank early in training and then use this experience to train the Q task policy for optimality as training progresses.
This is reasonable given that the options models may not cover the optimal policy but could serve as a good exploration algorithm throughout training.
As new unsupervised architectures and losses are discovered, we expect to narrow the gap between the optimal desired behaviors and the options bank.Learning visual entities from pixels is still a challenging open problem in unsupervised learning and computer vision.
We expect novel sampling schemes in our proposed architecture to improve the entity discovery results.
Other unsupervised video segmentation algorithms and discrete latent variable models could also be used to boost the discovery process.
structured exploration in deep reinforcement learning via unsupervised visual abstraction discovery and control