Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for learning and generalization of behaviors across related tasks, it generally relies on uninformed sampling of environments from an unknown, uncontrolled context distribution, thus missing the benefits of structured, sequential learning. We introduce a novel relative entropy reinforcement learning algorithm that gives the agent the freedom to control the intermediate task distribution, allowing for its gradual progression towards the target context distribution. Empirical evaluation shows that the proposed curriculum learning scheme drastically improves sample efficiency and enables learning in scenarios with both broad and sharp target context distributions in which classical approaches perform sub-optimally.

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that utilizes only simple and convergent maximum likelihood loss functions, while also being able to leverage off-policy data. Our proposed approach, which we refer to as advantage-weighted regression (AWR), consists of two standard supervised learning steps: one to regress onto target values for a value function, and another to regress onto weighted target actions for the policy. The method is simple and general, can accommodate continuous and discrete actions, and can be implemented in just a few lines of code on top of standard supervised learning methods. We provide a theoretical motivation for AWR and analyze its properties when incorporating off-policy data from experience replay. We evaluate AWR on a suite of standard OpenAI Gym benchmark tasks, and show that it achieves competitive performance compared to a number of well-established state-of-the-art RL algorithms. AWR is also able to acquire more effective policies than most off-policy algorithms when learning from purely static datasets with no additional environmental interactions. Furthermore, we demonstrate our algorithm on challenging continuous control tasks with highly complex simulated characters.

Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error. However, learning can be hindered if the goal of the learning, defined by the reward function, is "not optimal". We demonstrate that by setting the goal/target of competition in a counter-intuitive but intelligent way, instead of heuristically trying solutions through many hours the DRL simulation can quickly converge into a winning strategy. The ICRA-DJI RoboMaster AI Challenge is a game of cooperation and competition between robots in a partially observable environment, quite similar to the Counter-Strike game. Unlike the traditional approach to games, where the reward is given at winning the match or hitting the enemy, our DRL algorithm rewards our robots when in a geometric-strategic advantage, which implicitly increases the winning chances. Furthermore, we use Deep Q Learning (DQL) to generate multi-agent paths for moving, which improves the cooperation between two robots by avoiding the collision. Finally, we implement a variant A* algorithm with the same implicit geometric goal as DQL and compare results. We conclude that a well-set goal can put in question the need for learning algorithms, with geometric-based searches outperforming DQL in many orders of magnitude.

Combining No-regret and Q-learning Ian A. Kash University of Illinois, Chicago, IL Michael Sullins University of Illinois, Chicago, IL Katja Hofmann Microsoft Research, Cambridge, UK Abstract Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall. We seek to understand how to relax these requirements. As a first step, we introduce a simple algorithm, local no-regret learning (LONR), which uses a Q-learning-like update rule to allow learning without terminal states or perfect recall. We prove its convergence for the basic case of MDPs (and limited extensions of them) and present empirical results showing that it achieves last iterate convergence in a number of settings, most notably NoSDE games, a class of Markov games specifically designed to be challenging to learn where no prior algorithm is known to achieve convergence to a stationary equilibrium even on average. 1 Introduction V ersions of counterfactual regret minimization (CFR) [50] have found success in playing poker at human expert level [10, 41] as well as fully solving nontrivial versions of it [8]. CFR more generally can solve extensive form games of incomplete information. It works by using a no-regret algorithm to select actions. In particular, one copy of such an algorithm is used at each information set, which corresponds to the full history of play observed by a single agent. The resulting algorithm satisfies a global no-regret guarantee, so at least in two-player zero-sum games is guaranteed to converge to an optimal strategy through sufficient self-play. However, CFR does have limitations. It makes two strong assumptions which are natural for games such as poker, but limit applicability to further settings. First, it assumes that the agent has perfect recall, which in a more general context means that the state representation captures the full history of states visited (and so imposes a tree structure). Current RL domains may rarely repeat states due to their large state spaces, but they certainly do not encode the full history of states and actions. Second, it assumes that a terminal state is eventually reached and performs updates only after this occurs.

Abstract: We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller. The result is a flexible architecture that is applicable to a class of problems with periodic motion for which one has an insight into the class of trajectories that might lead to a desired behavior. We illustrate the basics of our architecture using a synthetic control problem, then go on to learn speed-controlled locomotion for a quadrupedal robot by using Deep Reinforcement Learning and Evolutionary Strategies. We demonstrate that a simple linear policy, when paired with a parametric Trajectory Generator for quadrupedal gaits, can induce walking behaviors with controllable speed from 4 -dimensional IMU observations alone, and can be learned in under 1000 rollouts. We also transfer these policies to a real robot and show locomotion with controllable forward velocity. Keywords: Reinforcement Learning, Control, Locomotion 1 Introduction The recent success of Deep Learning (DL) on simulated robotic tasks has opened an exciting research direction. Nevertheless, many robotic tasks such as locomotion still remain an open problem for learning-based methods due to their complexity or dynamics. From a Deep Learning (DL) perspective, one way to tackle these complex problems is by using more and more complex policies (such as recurrent networks). Unfortunately, more complex policies are harder to train and require even more training data which is often problematic for robotics.

Reinforcement learning is a branch of Machine Learning and AI. It takes a very specific approach to creating models to do certain things. The objective of reinforcement learning is to teach a computer/machine to perform a certain task with a high degree of success. It is also important to note what reinforcement learning isn't. These models are artificial specific intelligence (ASIs), meaning they can only perform very specific tasks.

We propose a new aggregation framework for approximate dynamic programming, which provides a connection with rollout algorithms, approximate policy iteration, and other single and multistep lookahead methods. The central novel characteristic is the use of a bias function $V$ of the state, which biases the values of the aggregate cost function towards their correct levels. The classical aggregation framework is obtained when $V\equiv0$, but our scheme works best when $V$ is a known reasonably good approximation to the optimal cost function $J^*$. When $V$ is equal to the cost function $J_{\mu}$ of some known policy $\mu$ and there is only one aggregate state, our scheme is equivalent to the rollout algorithm based on $\mu$ (i.e., the result of a single policy improvement starting with the policy $\mu$). When $V=J_{\mu}$ and there are multiple aggregate states, our aggregation approach can be used as a more powerful form of improvement of $\mu$. Thus, when combined with an approximate policy evaluation scheme, our approach can form the basis for a new and enhanced form of approximate policy iteration. When $V$ is a generic bias function, our scheme is equivalent to approximation in value space with lookahead function equal to $V$ plus a local correction within each aggregate state. The local correction levels are obtained by solving a low-dimensional aggregate DP problem, yielding an arbitrarily close approximation to $J^*$, when the number of aggregate states is sufficiently large. Except for the bias function, the aggregate DP problem is similar to the one of the classical aggregation framework, and its algorithmic solution by simulation or other methods is nearly identical to one for classical aggregation, assuming values of $V$ are available when needed.

Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. The agent needs to learn a latent representation together with a control policy to perform the task. Fitting a high-capacity encoder using a scarce reward signal is not only sample inefficient, but also prone to suboptimal convergence. Two ways to improve sample efficiency are to extract relevant features for the task and use off-policy algorithms. We dissect various approaches of learning good latent features, and conclude that the image reconstruction loss is the essential ingredient that enables efficient and stable representation learning in image-based RL. Following these findings, we devise an off-policy actor-critic algorithm with an auxiliary decoder that trains end-to-end and matches state-of-the-art performance across both model-free and model-based algorithms on many challenging control tasks. We release our code to encourage future research on image-based RL.

Tech giant Google's recent claim regarding quantum supremacy created a buzz in the computer science community and got global mainstream media talking about quantum computing breakthroughs. Yesterday Google fed the public's growing interest in the topic with a blog post introducing a study on improving quantum computation using classical machine learning. The qubit is the most basic constituent of quantum computing, and also poses one of the most significant challenges for the realization of near-term quantum computers. Various characteristics of qubits have made it challenging to control them. Google AI explains that issues such as imperfections in the control electronics can "impact the fidelity of the computation and thus limit the applications of near-term quantum devices."