Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see before learning to act. DARLA's vision is based on learning a disentangled representation of the observed environment. Once DARLA can see, it is able to acquire source policies that are robust to many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenarios, an effect that holds across a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC).

A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse coding objective for policy evaluation. Despite the non-convexity of this objective, we prove that all local minima are global minima, making the approach amenable to simple optimization strategies. We empirically show that it is key to use a supervised objective, rather than the more straightforward unsupervised sparse coding approach. We compare the learned representations to a canonical fixed sparse representation, called tile-coding, demonstrating that the sparse coding representation outperforms a wide variety of tilecoding representations.

When an agent cannot represent a perfectly accurate model of its environment's dynamics, model-based reinforcement learning (MBRL) can fail catastrophically. Planning involves composing the predictions of the model; when flawed predictions are composed, even minor errors can compound and render the model useless for planning. Hallucinated Replay (Talvitie 2014) trains the model to "correct" itself when it produces errors, substantially improving MBRL with flawed models. This paper theoretically analyzes this approach, illuminates settings in which it is likely to be effective or ineffective, and presents a novel error bound, showing that a model's ability to self-correct is more tightly related to MBRL performance than one-step prediction error. These results inspire an MBRL algorithm for deterministic MDPs with performance guarantees that are robust to model class limitations.

Autonomous navigation is one of the core problems in mobile robotics. It can roughly be characterized as the ability of a robot to get from its current position to a designated goal location solely based on the input it receives from its on-board sensors. A popular approach to this problem relies on the successful combination of a series of different algorithms for the problems of simultaneous localization and mapping (SLAM), localization in a given map as well as path planning and control, all of which often depend on additional information given to the agent. Although individually the problems of SLAM, localization, path planning and control are well understood [1], [2], [3], and a lot of progress has been made on learning control [4], they have mainly been treated as separable problems within robotics and some often require human assistance during setup-time. For example, the majority of SLAM solutions are implemented as passive procedures relying on special exploration strategies or a human controlling the robot for sensory data acquisition. In addition, they typically require an expert to check as to whether the obtained map is accurate enough for path planning and localization. Our goal in this paper is to make first steps towards a solution for navigation tasks without explicit localization, mapping and path planning procedures.

Google's AI boutique, DeepMind, known for dispelling human delusions of intellectual superiority by soundly beating the world's top Go players with computer code, has found that instilling its software agents with something like imagination helps them learn better. In two papers published this week – "Imagination-Augmented Agents for Deep Reinforcement Learning" and "Learning model-based planning from scratch" – the AI biz's brain boffins, based in Britain, describe novel techniques for improving deep reinforcement learning through what can generously be described as imaginative planning. Reinforcement learning is a form of machine learning. It involves a software agent that learns by interacting with a specific environment, usually through trial and error. Deep learning is a form of machine that involves algorithms inspired by the human brain, called neural networks.

In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behaviour. We begin with theoretical results in both the policy evaluation and control settings, exposing a significant distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman's equation to the learning of approximate value distributions. We evaluate our algorithm using the suite of games from the Arcade Learning Environment. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning. Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting.

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to learning maximum entropy policies, resulting into a new algorithm, called soft Q-learning, that expresses the optimal policy via a Boltzmann distribution. We use the recently proposed amortized Stein variational gradient descent to learn a stochastic sampling network that approximates samples from this distribution. The benefits of the proposed algorithm include improved exploration and compositionality that allows transferring skills between tasks, which we confirm in simulated experiments with swimming and walking robots. We also draw a connection to actor-critic methods, which can be viewed performing approximate inference on the corresponding energy-based model.

Ethics are an Issue Don't kid yourself--introducing self-learning robots that can learn faster and better than humans will come with a huge range of issues. On our end, we can only program them to the extent of our human knowledge, which is always going to be limited. If we forget to set system safeties, we could have serious trouble on our hands in terms of public safety. On the other end, the question remains: do we really want to create a world of computers that think--and do--via their own free will, especially when they are smarter than humans? That's definitely an issue we need to reflect on before jumping too far into the reinforcement learning landscape.

Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting. To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required. We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.