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Title: Diversity is All You Need: Learning Skills without a Reward Function

Paper content:
Intelligent creatures can explore their environments and learn useful skills without supervision.
In this paper, we propose ``Diversity is All You Need''(DIAYN), a method for learning useful skills without a reward function.
Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy.
On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping.
In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward.
We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks.
Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.
Deep reinforcement learning (RL) has been demonstrated to effectively learn a wide range of rewarddriven skills, including playing games (Mnih et al., 2013; Silver et al., 2016) , controlling robots (Gu et al., 2017; Schulman et al., 2015b) , and navigating complex environments (Zhu et al., 2017; Mirowski et al., 2016) .
However, intelligent creatures can explore their environments and learn useful skills even without supervision, so that when they are later faced with specific goals, they can use those skills to satisfy the new goals quickly and efficiently.Learning skills without reward has several practical applications.
Environments with sparse rewards effectively have no reward until the agent randomly reaches a goal state.
Learning useful skills without supervision may help address challenges in exploration in these environments.
For long horizon tasks, skills discovered without reward can serve as primitives for hierarchical RL, effectively shortening the episode length.
In many practical settings, interacting with the environment is essentially free, but evaluating the reward requires human feedback BID7 .
Unsupervised learning of skills may reduce the amount of supervision necessary to learn a task.
While we can take the human out of the loop by designing a reward function, it is challenging to design a reward function that elicits the desired behaviors from the agent (Hadfield-Menell et al., 2017) .
Finally, when given an unfamiliar environment, it is challenging to determine what tasks an agent should be able to learn.
Unsupervised skill discovery partially answers this question.
1 Autonomous acquisition of useful skills without any reward signal is an exceedingly challenging problem.
A skill is a latent-conditioned policy that alters the state of the environment in a consistent way.
We consider the setting where the reward function is unknown, so we want to learn a set of skills by maximizing the utility of this set.
Making progress on this problem requires specifying a learning objective that ensures that each skill individually is distinct and that the skills collectively explore large parts of the state space.
In this paper, we show how a simple objective based on mutual information can enable RL agents to autonomously discover such skills.
These skills are useful for a number of applications, including hierarchical reinforcement learning and imitation learning.We propose a method for learning diverse skills with deep RL in the absence of any rewards.
We hypothesize that in order to acquire skills that are useful, we must train the skills so that they maximize coverage over the set of possible behaviors.
While one skill might perform a useless behavior like random dithering, other skills should perform behaviors that are distinguishable from random dithering, and therefore more useful.
A key idea in our work is to use discriminability between skills as an objective.
Further, skills that are distinguishable are not necessarily maximally diverse -a slight difference in states makes two skills distinguishable, but not necessarily diverse in a semantically meaningful way.
To combat this problem, we want to learn skills that not only are distinguishable, but also are as diverse as possible.
By learning distinguishable skills that are as random as possible, we can "push" the skills away from each other, making each skill robust to perturbations and effectively exploring the environment.
By maximizing this objective, we can learn skills that run forward, do backflips, skip backwards, and perform face flops (see Figure 3 ).
Our paper makes five contributions.
First, we propose a method for learning useful skills without any rewards.
We formalize our discriminability goal as maximizing an information theoretic objective with a maximum entropy policy.
Second, we show that this simple exploration objective results in the unsupervised emergence of diverse skills, such as running and jumping, on several simulated robotic tasks.
In a number of RL benchmark environments, our method is able to solve the benchmark task despite never receiving the true task reward.
In these environments, some of the learned skills correspond to solving the task, and each skill that solves the task does so in a distinct manner.
Third, we propose a simple method for using learned skills for hierarchical RL and find this methods solves challenging tasks.
Four, we demonstrate how skills discovered can be quickly adapted to solve a new task.
Finally, we show how skills discovered can be used for imitation learning.
In this paper, we present DIAYN, a method for learning skills without reward functions.
We show that DIAYN learns diverse skills for complex tasks, often solving benchmark tasks with one of the learned skills without actually receiving any task reward.
We further proposed methods for using the learned skills (1) to quickly adapt to a new task, (2) to solve complex tasks via hierarchical RL, and (3) to imitate an expert.
As a rule of thumb, DIAYN may make learning a task easier by replacing the task's complex action space with a set of useful skills.
DIAYN could be combined with methods for augmenting the observation space and reward function.
Using the common language of information theory, a joint objective can likely be derived.
DIAYN may also more efficiently learn from human preferences by having humans select among learned skills.
Finally, the skills produced by DIAYN might be used by game designers to allow players to control complex robots and by artists to animate characters.

assistant: text: We propose an algorithm for learning useful skills without a reward function, and show how these skills can be used to solve downstream tasks.