The success of automated driving deployment is highly depending on the ability to develop an efficient and safe driving policy. The problem is well formulated under the framework of optimal control as a cost optimization problem. Model based solutions using traditional planning are efficient, but require the knowledge of the environment model. On the other hand, model free solutions suffer sample inefficiency and require too many interactions with the environment, which is infeasible in practice. Methods under the Reinforcement Learning framework usually require the notion of a reward function, which is not available in the real world. Imitation learning helps in improving sample efficiency by introducing prior knowledge obtained from the demonstrated behavior, on the risk of exact behavior cloning without generalizing to unseen environments. In this paper we propose a Meta learning framework, based on data set aggregation, to improve generalization of imitation learning algorithms. Under the proposed framework, we propose MetaDAgger, a novel algorithm which tackles the generalization issues in traditional imitation learning. We use The Open Race Car Simulator (TORCS) to test our algorithm. Results on unseen test tracks show significant improvement over traditional imitation learning algorithms, improving the learning time and sample efficiency in the same time. The results are also supported by visualization of the learnt features to prove generalization of the captured details.

Reinforcement Learning (RL) is the task of training an agent to perform optimally in an environment using the reward and observation signals perceived upon taking actions which change the environment dynamics. Learning optimal behavior is inherently difficult because of challenges like credit assignment and exploration-exploitation trade offs that need to be made while converging to a solution. In many scenarios, like training a rover to move on a Martian surface, the cost of obtaining samples for learning can be high (in terms of robot's energy expenditure etc.), and so sample efficiency is an important subproblem which deserves special attention. Very often it is the case that the environment has intrinsic symmetries which can be leveraged by the agent to improve performance and learn more efficiently. For example, in the Cart-Pole domain [1, 2] the state action space is symmetric with respect to reflection about the plane perpendicular to the direction of motion of the cart (Figure 1). In fact, in many environments, the number of symmetry relations tend to increase with the dimensionality of the state space. For instance, for the simple case of grid world of dimension d (Figure 1) there exist O(d!2

Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy. In this paper, we focus on policy evaluation with linear function approximation over a fixed dataset. We first transform the empirical policy evaluation problem into a (quadratic) convex-concave saddle point problem, and then present a primal-dual batch gradient method, as well as two stochastic variance reduction methods for solving the problem. These algorithms scale linearly in both sample size and feature dimension. Moreover, they achieve linear convergence even when the saddle-point problem has only strong concavity in the dual variables but no strong convexity in the primal variables. Numerical experiments on benchmark problems demonstrate the effectiveness of our methods.

This repository contains code to train and run a self-driving truck in Euro Truck Simulator 2. The resulting AI will automatically steer, accelerate and brake. It is trained (mostly) via reinforcement learning and only has access to the buttons W, A, S and D (i.e. it can not directly set the steering wheel angle). The basic training method follows the standard reinforcement learning approach from the original Atari paper. Additionally, a separation of Q-values in V (value) and A (advantage) - as described in Dueling Network Architectures for Deep Reinforcement Learning - is used. Further, the model tries to predict future states and rewards, similar to the description in Deep Successor Reinforcement Learning.

In several realistic situations, an interactive learning agent can practice and refine its strategy before going on to be evaluated. For instance, consider a student preparing for a series of tests. She would typically take a few practice tests to know which areas she needs to improve upon. Based of the scores she obtains in these practice tests, she would formulate a strategy for maximizing her scores in the actual tests. We treat this scenario in the context of an agent exploring a fixed-horizon episodic Markov Decision Process (MDP), where the agent can practice on the MDP for some number of episodes (not necessarily known in advance) before starting to incur regret for its actions. During practice, the agent's goal must be to maximize the probability of following an optimal policy. This is akin to the problem of Pure Exploration (PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE), which is similar to its bandit counterpart. We show that the Bayesian simple regret converges at an optimal exponential rate when using PSPE. When the agent starts being evaluated, its goal would be to minimize the cumulative regret incurred. This is akin to the problem of Reinforcement Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning algorithm (PSRL) initialized with the posteriors of the practice phase. We hypothesize that this PSPE + PSRL combination is an optimal strategy for minimizing regret in RL problems with an initial practice phase. We show empirical results which prove that having a lower simple regret at the end of the practice phase results in having lower cumulative regret during evaluation.

Google Brain researcher Douglas Eck will discuss Magenta, a project using TensorFlow to generate art and music with deep nets and reinforcement learning. He'll also talk about how artists and musicians fit in to the effort. We'll dive into some of the technical details and challenges faced in building generative models, but no machine learning expertise is required to follow the session. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. However, in some cases an agent may be able to gain experience in the domain prior to being given a task. In such cases, intrinsic motivation can be used to enable the agent to learn a useful model of the environment that is likely to help it learn its eventual tasks more efficiently. This paradigm fits robots particularly well, as they need to learn about their own dynamics and affordances which can be applied to many different tasks. The algorithm learns models of the transition dynamics of a domain using random forests.

I wanted to like this paper. Curriculum learning is an area that needs more research, and automating the curriculum process is a good idea. However, the use of a GAN here is completely overkill -- the space of goals here is low dimensional and relatively unstructured. I'd wager any generative modeling technique would've worked for their experiments.

Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and associated approximate models, applied to this context, still assume Markovian state transitions. In this paper, we explore the use of a recently proposed attention-based model, the Gated End-to-End Memory Network, for sequential control. We call the resulting model the Gated End-to-End Memory Policy Network. More precisely, we use a model-free value-based algorithm to learn policies for partially observed domains using this memory-enhanced neural network. This model is end-to-end learnable and it features unbounded memory. Indeed, because of its attention mechanism and associated non-parametric memory, the proposed model allows us to define an attention mechanism over the observation stream unlike recurrent models. We show encouraging results that illustrate the capability of our attention-based model in the context of the continuous-state non-stationary control problem of stock trading. We also present an OpenAI Gym environment for simulated stock exchange and explain its relevance as a benchmark for the field of non-Markovian decision process learning.

Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience. We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables better learning of value functions as compared to other forms of experience replay. Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful.