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Title: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Paper content:
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.
While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.
Current methods rely heavily on on-policy experience, limiting their sample efficiency.
They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.
In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control.
In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.
This probabilistic interpretation enables posterior sampling for structured and efficient exploration.
We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.
Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.
Learning large repertoires of behaviors with conventional RL methods quickly becomes prohibitive as learning each task often requires millions of interactions with the environment.
Fortunately, many of the problems we would like our autonomous agents to solve share common structure.
For example screwing a cap on a bottle and turning a doorknob both involve grasping an object in the hand and rotating the wrist.
Exploiting this structure to learn new tasks more quickly remains an open and pressing topic.While meta-learned policies adapt to new tasks with only a few trials, during training they require massive amounts of data drawn from a large set of distinct tasks, exacerbating the problem of sample efficiency that plagues RL algorithms.
Most current meta-RL methods require on-policy data during both meta-training and adaptation BID3 ; BID26 ; BID2 ; BID13 ; BID16 ; , rendering them exceedingly inefficient during meta-training.
However, making use of off-policy data for meta-RL poses new challenges.
Meta-learning typically operates on the principle that meta-training time should match meta-test time.
This makes it inherently difficult to meta-train a policy to adapt from off-policy data, which is systematically different from the data the policy would see when it explores (on-policy) in a new task at meta-test time.To achieve both adaptation and meta-training data efficiency, our approach integrates online inference of probabilistic context variables with existing off-policy RL algorithms.
During meta-training, we learn a probabilistic encoder that accumulates the necessary statistics from past experience that enable the policy to perform the task.
At meta-test time, our method adapts quickly by sampling context variables ("task hypotheses"), acting according to that task, and then updating its belief about the task by updating the posterior over the context variables.
Our approach integrates easily with existing off-policy RL algorithms, enabling good sample efficiency during meta-training.The primary contribution of our work is an off-policy meta-RL algorithm Probabilistic Embeddings for Actor-critic RL (PEARL) that achieves excellent sample efficiency during meta-training, enables fast adaptation by accumulating experience online, and performs structured exploration by reasoning about uncertainty over tasks.
We demonstrate 20-100X improvement in meta-training sample efficiency on six continuous control meta-learning environments, and demonstrate how our model structured exploration to adapt rapidly to new tasks with sparse rewards.

assistant: text: Sample efficient meta-RL by combining variational inference of probabilistic task variables with off-policy RL