The ability to learn effective transformations of complex data sources, sometimes called representation learning, is an essential primitive in modern machine learning, leading to remarkable achievements in language modeling, vision, and serving as a partial explanation for the success of deep learning more broadly (Bengio et al., 2013). In Reinforcement Learning (RL), several works have shown empirically that learning succinct representations of perceptual inputs can accelerate the search for decision-making policies (Pathak et al., 2017; Tang et al., 2017; Oord et al., 2018; Srinivas et al., 2020). However, representation learning for RL is far more subtle than it is for supervised learning (Du et al., 2019a; Van Roy and Dong, 2019; Lattimore and Szepesvari, 2019), and the theoretical foundations of representation learning for RL are nascent. The first question that arises in this context is: what is a good representation? Intuitively, a good representation should help us achieve greater sample efficiency on downstream tasks.

In a study printed on the preprint server Arxiv.org, DeepMind researchers describe a reinforcement learning algorithm-generating approach that discovers what to foretell and the way to be taught it by interacting with environments. They declare the generated algorithms carry out nicely on a variety of difficult Atari video video games, reaching "non-trivial" efficiency indicative of the approach's generalizability. Reinforcement studying algorithms -- algorithms that allow software program brokers to be taught in environments by trial and error utilizing suggestions -- replace an agent's parameters in response to one in all a number of guidelines. These guidelines are often found via years of analysis, and automating their discovery from knowledge might result in extra environment friendly algorithms, or algorithms higher tailored to particular environments. DeepMind's answer is a meta-learning framework that collectively discovers what a specific agent ought to predict and the way to use the predictions for coverage enchancment.

It is well established that machine learning models perform better with well-curated large scale data. However, collecting and curating is one of the biggest challenges right now. There are billion-dollar companies like Scale.ai who set up their shop with the sole purpose to annotate data. The whole data collection process is so tedious that it has become profitable for few. But, we are still talking about what happens before data arrives at an ML pipeline.

Model-free deep reinforcement learning (RL) has been successful in a range of challenging domains. However, there are some remaining issues, such as stabilizing the optimization of nonlinear function approximators, preventing error propagation due to the Bellman backup in Q-learning, and efficient exploration. To mitigate these issues, we present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy RL algorithms. SUNRISE integrates three key ingredients: (a) bootstrap with random initialization which improves the stability of the learning process by training a diverse ensemble of agents, (b) weighted Bellman backups, which prevent error propagation in Q-learning by reweighing sample transitions based on uncertainty estimates from the ensembles, and (c) an inference method that selects actions using highest upper-confidence bounds for efficient exploration. Our experiments show that SUNRISE significantly improves the performance of existing off-policy RL algorithms, such as Soft Actor-Critic and Rainbow DQN, for both continuous and discrete control tasks on both low-dimensional and high-dimensional environments. Our training code is available at https://github.com/pokaxpoka/sunrise.

Designing efficient algorithms that learn and plan in sequential decision-making tasks with large state and action spaces has become the central goal of modern reinforcement learning (RL) in recent years. Due to numerous possible states and actions, traditional tabular reinforcement learning methods (Watkins, 1989; Jaksch et al., 2010; Azar et al., 2017) which directly access each stateaction pair are computationally intractable. A common method to design reinforcement learning algorithms for large-scale state and action spaces is to make use of feature mappings such as linear functions or neural networks to map states and actions to a low-dimensional space and solve the decision-making problem in the feature space. Despite the empirical success of feature mapping based reinforcement learning methods (Singh et al., 1995; Kwok and Fox, 2004; Bertsekas, 2018), the theoretical understanding and the fundamental limits of these methods remain largely understudied. In this paper, we aim to develop provable reinforcement learning algorithms with feature mapping for discounted Markov Decision Processes (MDPs). Discounted MDP is one of the most widely used models to formulate the modern reinforcement learning tasks such as Atari games (Mnih et al., 2015) and deep recommendation system (Zheng et al., 2018).

Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framework based on the OpenAI Gym framework, and demonstrate its use on a simple hospital bed capacity model. We built the Deep RL agents using PyTorch, and the Hospital Simulatation using SimPy. Results: We demonstrate example models using a Double Deep Q Network or a Duelling Double Deep Q Network as the Deep RL agent. Conclusion: SimPy may be used to create Health System Simulations that are compatible with agents developed and tested on OpenAI Gym environments. GitHub repository of code: https://github.com/MichaelAllen1966/learninghospital

Deep Reinforcement Learning (RL) experiments are commonly performed in simulated environment, due to the tremendous training sample demand from deep neural networks. However, model-based Deep Bayesian RL, such as Deep PILCO, allows a robot to learn good policies within few trials in the real world. Although Deep PILCO has been applied on many single-robot tasks, in here we propose, for the first time, an application of Deep PILCO on a multi-robot confrontation game, and compare the algorithm with a model-free Deep RL algorithm, Deep Q-Learning. Our experiments show that Deep PILCO significantly outperforms Deep Q-Learning in learning efficiency and scalability. We conclude that sample-efficient Deep Bayesian learning algorithms have great prospects on competitive games where the agent aims to win the opponents in the real world, as opposed to simulated applications.

Off-policy reinforcement learning (RL) holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting -- where a fixed collection of interactions are provided and no further interactions are allowed -- it has been shown that standard off-policy RL methods can significantly underperform. Recently proposed methods aim to address this shortcoming by regularizing learned policies to remain close to the given dataset of interactions. However, these methods involve several configurable components such as learning a separate policy network on top of a behavior cloning actor, and explicitly constraining action spaces through clipping or reward penalties. Striving for simultaneous simplicity and performance, in this work we present a novel backup operator, Expected-Max Q-Learning (EMaQ), which naturally restricts learned policies to remain within the support of the offline dataset \emph{without any explicit regularization}, while retaining desirable theoretical properties such as contraction. We demonstrate that EMaQ is competitive with Soft Actor Critic (SAC) in online RL, and surpasses SAC in the deployment-efficient setting. In the offline RL setting -- the main focus of this work -- through EMaQ we are able to make important observations regarding key components of offline RL, and the nature of standard benchmark tasks. Lastly but importantly, we observe that EMaQ achieves state-of-the-art performance with fewer moving parts such as one less function approximation, making it a strong, yet easy to implement baseline for future work.

In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep RL community. We embed several recent techniques in our algorithm that improve the original Deep Q-Networks (DQN) for discrete control and discuss the traffic-related interpretations that follow. We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making. We introduce a new form of reward function which is further discussed using illustrative examples with comparisons to traditional traffic control methods.

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with online data collection in the real-world, including cost, safety, or ethical concerns. In this paper, we propose a benchmark called RL Unplugged to evaluate and compare offline RL methods. RL Unplugged includes data from a diverse range of domains including games (e.g., Atari benchmark) and simulated motor control problems (e.g., DM Control Suite). The datasets include domains that are partially or fully observable, use continuous or discrete actions, and have stochastic vs. deterministic dynamics. We propose detailed evaluation protocols for each domain in RL Unplugged and provide an extensive analysis of supervised learning and offline RL methods using these protocols. We will release data for all our tasks and open-source all algorithms presented in this paper. We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community. Moving forward, we view RL Unplugged as a living benchmark suite that will evolve and grow with datasets contributed by the research community and ourselves. Our project page is available on https://git.io/JJUhd.