RL problems through the idea of "learning to learn". Current meta-RL methods can be classified in to two categories. These methods mainly differ in their ways of inference [3, 4, 20]. The other line follows the technique of relabeling that enables sample reuse across tasks, i.e., learning a task Packer et al. apply hindsight relabeling for meta-RL, and propose hindsight task relabeling (HTR) to relabel the trajectories Taking a step further than hindsight relabelling, Wan et al. introduce additionally foresight Huang et al. derive a general form of policy gradient from DR value estimator [29], whereas a DR off-policy actor-critic Kallus et al. propose the doubly robust method to find a robust policy that can Depending on the knowledge to be transferred, these methods in RL can be roughly divided into classes including sampled transitions [32, 33], learned policies or value networks [34, 35, 36, 37], features [38, 39, 40], and skills [41, 42]. Doubly Robust Property for Direct Use of Doubly Robust Estimator We show the doubly robust property of the DR estimator for value function in Eq. (5) in the main text, as follows.

Imitation learning has been commonly applied to solve different tasks in isolation.

Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks. In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly. The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy.

Despite their wide success in a variety of domains, recurrent neural networks (RNNs) are often inhibited by the difficulty of learning an internal state representation.

Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years.

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It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms.

We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.

We also provide a general convergence analysis to support our empirical findings. Although our analysis is similar to CPI's, it has a key difference: as long as MBOC succeeds, we can provide a larger policy improvement than CPI at each iteration.

Can "useful" pre-training tasks be discovered in