We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics to train a robot directly from joint states, using traditional robotic tools. We use an state-of-the-art implementation of the Proximal Policy Optimization, Trust Region Policy Optimization and Actor-Critic Kronecker-Factored Trust Region algorithms to learn policies in four different Modular Articulated Robotic Arm (MARA) environments. We support this process using a framework that communicates with typical tools used in robotics, such as Gazebo and Robot Operating System 2 (ROS 2). We evaluate several algorithms in modular robots with an empirical study in simulation.

Before I explain what Q Learning is, I will quickly explain the basic principle of reinforcement learning. Reinforcement learning is a category of machine learning algorithms where the systems learn on their own by interacting with the environment. The idea is that a reward is provided to the agent if the action it takes is correct. Otherwise, some penalty is assigned to discourage the action. It is similar to how we train dogs to perform tricks, give it a snack for successfully doing a roll and rebuke it for dirtying your carpet.

If you shop on Amazon, an algorithm rather than a human probably set the price of the service or item you bought. Pricing algorithms have become ubiquitous in online retail as automated systems have grown increasingly affordable and easy to implement. But while companies like airlines and hotels have long used machines to set their prices, pricing systems have evolved. They have moved from rule-based programs to reinforcement-learning ones, where the logic of deciding a product's price is no longer within a human's control. If you recall, reinforcement learning is a subset of machine learning that uses penalties and rewards to incentivize an AI agent toward a specific goal.

In this work we develop a new off-policy actor-critic algorithm that performs policy improvement with convergence guarantees in the multi-agent setting using function approximation. To achieve this, we extend the method of emphatic temporal differences (ETD(λ)) to the multi-agent setting with provable convergence under linear function approximation, and we also derive a novel off-policy policy gradient theorem for the multi-agent setting. Using these new results, we develop our two-timescale algorithm, which uses ETD(λ) to perform policy evaluation for the critic step at a faster timescale and policy gradient ascent using emphatic weightings for the actor step at a slower timescale. We also provide convergence guarantees for the actor step. Our work builds on recent advances in three main areas: multi-agent on-policy actor-critic methods, emphatic temporal difference learning for off-policy policy evaluation, and the use of emphatic weightings in off-policy policy gradient methods.

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Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm. RL provides solution methods for sequential decision making problems as well as those can be transformed into sequential ones. RL connects deeply with optimization, statistics, game theory, causal inference, sequential experimentation, etc., overlaps largely with approximate dynamic programming and optimal control, and applies broadly in science, engineering and arts. RL has been making steady progress in academia recently, e.g., Atari games, AlphaGo, visuomotor policies for robots. RL has also been applied to real world scenarios like recommender systems and neural architecture search. See a recent collection about RL applications.

In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our method achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.

From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines

Finding a set of edges that minimizes expected One of the main challenges in reinforcement learning cover time is an extremely hard combinatorial optimization is solving tasks with sparse reward. We show problem (Braess, 1968; Braess et al., 2005). Thus, our that the difficulty of discovering a distant rewarding algorithm instead seeks to minimize the upper bound of the state in an MDP is bounded by the expected expected cover time given as a function of the algebraic cover time of a random walk over the graph induced connectivity of the graph Laplacian (Fiedler, 1973; Broder by the MDP's transition dynamics. We & Karlin, 1989; Chung, 1996) using the heuristic method therefore propose to accelerate exploration by constructing by Ghosh & Boyd (2006) that improves the upper bound of options that minimize cover time. The the expected cover time of a uniform random walk.

We aim to jointly optimize the antenna tilt angle, and the vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet) via a synergistic combination of deep learning (DL) and reinforcement learning (RL). The interactions between the cells, most notably due to their coupled interference and the large number of users, renders this optimization problem prohibitively complex. This makes the proposed deep RL technique attractive as a practical online solution for real deployments, which should automatically adapt to new base stations being added and other environmental changes in the network. In the proposed algorithm, DL is used to extract the features by learning the locations of the users, and mean field RL is used to learn the average interference values for different antenna settings. Our results illustrate that the proposed deep RL algorithm can approach the optimum weighted sum rate with hundreds of online trials, as opposed to millions of trials for standard Q-learning, assuming relatively low environmental dynamics. Furthermore, the proposed algorithm is compact and implementable, and empirically appears to provide a performance guarantee regardless of the amount of environmental dynamics.