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:

Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning.
In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.
The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning.
Many of previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data.
Instantiating our framework results in a generalizable policy to unseen tasks.
Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks and video games.
While deep Reinforcement Learning (RL) methods have shown impressive performance on video games (Mnih et al., 2015) and robotics tasks (Schulman et al., 2015; Lillicrap et al., 2015) , they solve each problem tabula rasa.
Hence, it will be hard for them to generalize to new tasks without re-training even due to small changes.
However, humans can quickly adapt their skills to a new task that requires similar priors e.g. physics, semantics, affordances to past experience.
The priors can be learned from a spectrum of examples ranging from perfect demonstrative ones that accomplish certain tasks to aimless exploration.
A parameterized intelligent agent "Mario" which learns to move to the right in the upper level in Figure 1 would fail to transfer the priors from to the lower level in Figure 1 and further play the game in the new level because change of configurations and background, e.g. different shapes of ladder, new fence.
When an inexperienced human player is controlling the Mario to move it to the right in the upper level, it might take many trials for him/her to realize the falling to a pit and approaching the "koopa"(turtle) from the left are harmful while standing on the top of the "koopa"(turtle) is not.
However, once learned, s/he can infer similar mechanisms in the lower level in Figure 1 without additional trials because human have a variety of priors including the concept of object, similarity, semantics, affordance, etc (Gibson, 2014; Dubey et al., 2018) .
In this paper, we intend to teach machine agents to realize and utilize useful priors to generalize to new tasks without finetuning.
Toward addressing the generalization problem with learned priors, we follow the intuition that: (1) each trajectory in a video game or a robotics task is composed of state-action pairs with object interactions (2) terminal rewards can be approximated by the aggregation of the scores for each state-action pairs.
With those intuitions in mind, we summarize our proposed method in broad strokes.
Given a trajectory with a terminal sparse reward, we first parameterize the score of an action-local region pair with a convolutional neural network F and then aggregate the scores to approximate the final sparse reward.
To further enable actionable agents to utilize the scores, a neural dynamics model In this paper, the agent focuses on learning two types of priors: learning an action-state preference score for local regions and a dynamics model.
The action-state scores on the middle left learns that approaching the "Koopa" from the left is undesirable while from the top is desirable.
On the middle right, a dynamics model can be learned to predict a future state based on the current state and action.
The agent can apply the priors to a new task World 2 Stage 1 to achieve reasonable policy with zero shot.
can be learned from the interaction data using self-supervision.
We show that how an agent can take advantage of the scoring function and the learned dynamics model with planning algorithms (Mayne et al., 2000) .
We adopt the sparse terminal reward setting because in most of the tasks, step-by-step rewards are hard to obtain while final evaluations for trajectories are relatively easy.
Readers may argue that learning a dense score for every interaction step is reminiscent of Inverse Reinforcement Learning (Ng et al., 2000; Abbeel & Ng, 2004) .
The distinctions between the proposed method and IRL are threefold: First, instead of learning a reward function of state s, we learn a scoring function of a local state s l and an action a, which is sufficiently rich in a physical environment and experimentally can generalize well.
Second, with the scoring function in hand, we use a dynamics model learned from passive data to obtain the actual policy in a model-based manner while IRL needs to re-train an agent that can be as data inefficient as model-free RL.
However, IRL can have difficulty learning a useful model because the expert demonstrations usually only cover a small portion of the true dynamics.
Third, we eliminate the assumption of expensive expert demonstrations with the cost of adding a relatively economical sparse reward in the end.
This elimination not only reduces the cost for data collection, but also includes more diverse data to train a robust model.
The proposed scoring function, beyond being a cost function for planning, can also be treated as an indicator of the existence of objects, which affect the evaluation of a trajectory.
We empirically evaluate the scores for objects extracted in the context of human priors and hence find the potential of using our method as an unsupervised method for object discovery.
In this paper, we have three major contributions.
First, we propose a framework that can learn task-agnostic priors that can generalize to novel tasks.
Second, we incorporate a self-supervised learned dynamics model with scoring function to learn a useful policy.
Third, we demonstrate the effectiveness of the proposed method on a didactic grid-world example, a well-known video game "Super Mario Bros" and a robotics Blocked-Reach environment and show our method outperforms various baselines.
Last but not least, we find that objects emerge from our method in an unsupervised manner, which could be useful to other visual tasks.
We devise a novel Scoring-Aggregating-Planning (SAP) framework for designing algorithms that can learn generalizable priors from exploration and sparse rewards for novel tasks.
We find the proposed method can capture the transferable priors and take advantage of the priors without any finetuning.
Experimental results also show that following the SAP framework, designed algorithms outperform a variety of baseline methods on both training and unseen testing tasks in different application domains.
While this paper explores some applications under SAP framework, many compelling questions remain open.
There is a welcoming avenue for future work to improve each component of the framework.
For example, for complex tasks, there might be priors beyond contact force and game dynamics with a much more complicated action space.
Hence, how to extract relational priors from them to solve novel tasks is still yet to be explored.
Dubey et al. (2018) thoroughly studied existing human priors in video game playing; however, it is still not clear how to use the correct priors for real-world applications in an SAP framework (e.g. fire is good when you want to look but harmful while being too close).
There are many interactive samples in the real world, but most of them are suboptimal.
However, an evaluation of them can be given from a task-specific score or human evaluation.
Our method excels in this setting.
In theory, it can be extended to the case a binary sparse reward is given by carefully choosing an aggregator such as logic operators with sufficient samples.
We leave those extensions for future works.
A EXPERIMENT SPECS A.1
HIDDEN REWARD GRIDWORLD A.1.1 ENVIRONMENT.
In Figure 3a , we visualize a sample of the gridworld environment.
Each entry correspond to a noised feature vector based on the type of object in it.
Each feature is a length 16 vector whose entries are uniformly sampled from [0, 1].
Upon each feature, we add a small random noise from a normal distribution with µ = 0, σ = 0.05.
The outer-most entries correspond to padding objects whose rewards are 0.
The action space includes move toward four directions up, down, left, right.
If an agent attempts to take an action which leads to outside of our grid, it will be ignored be the environment.
We learn dense scores and dynamics model as priors from exploration data and use them to induce a good policy in new tasks in zero-shot condition.