To avoid the paper being thrown in the bin we provide this with a large, negative reward, say -1, and because the teacher is please with it being placed in the bin this nets a large positive reward, 1. To avoid the outcome where it continually gets passed around the room, we set the reward for all other actions to be a small, negative value, say -0.04. If we set this as a positive or null number then the model may let the paper go round and round as it would be better to gain small positives than risk getting close to the negative outcome. This number is also very small as it will only collect a single terminal reward but it could take many steps to end the episode and we need to ensure that, if the paper is place in the bin, the positive outcome is not cancelled out. Please note, the rewards are always relative to one another and I have chosen arbitrary figures but these can be changed if the results are not as desired.

Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL) is a sub-field within machine learning that is concerned with learning how to make sequences of decisions so as to optimize long-term effects. Already, RL algorithms have been proposed to identify decision-making strategies for mechanical ventilation [Prasad et al., 2017], sepsis management [Raghu et al., 2017] and treatment of schizophrenia [Shortreed et al., 2011]. However, before implementing treatment policies learned by black-box algorithms in highstakes clinical decision problems, special care must be taken in the evaluation of these policies. Specifically, we focus on the observational setting, that is, the setting in which our RL algorithm has proposed some treatment policy, and we want to evaluate it based on historical data. This setting is common in healthcare applications, where we do not wish to experiment with patients' lives without evidence that the proposed treatment strategy may be better than current practice. While formal statistical methods have been developed to assess the quality of new policies based on observational data alone [Thomas and Brunskill, 2016, Precup et al., 2000, Pearl, 2009, Imbens and Rubin, 2015], these methods rely on strong assumptions and are limited by statistical properties. We do not attempt to summarize this vast literature in this work, rather, we aim to provide a conceptual starting point for clinical and computational researchers to ask the right questions when designing and evaluating algorithms for new ways of treating patients. In the following, we describe how choices about how to summarize a history, variance of statistical estimators, and confounders in more ad-hoc measures can result in unreliable, even misleading estimates of the quality of a treatment policy.

Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance, especially those with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g. 25 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task).

We propose a new way of deriving policy gradient updates for reinforcement learning. Our technique, based on Fourier analysis, recasts integrals that arise with expected policy gradients as convolutions and turns them into multiplications. The obtained analytical solutions allow us to capture the low variance benefits of EPG in a broad range of settings. For the critic, we treat trigonometric and radial basis functions, two function families with the universal approximation property. The choice of policy can be almost arbitrary, including mixtures or hybrid continuous-discrete probability distributions. Moreover, we derive a general family of sample-based estimators for stochastic policy gradients, which unifies existing results on sample-based approximation. We believe that this technique has the potential to shape the next generation of policy gradient approaches, powered by analytical results.

Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animal's behavioral strategy from behavioral time-series data. We applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, fed worms prefer, while starved worms avoid the cultivation temperature on a thermal gradient.

In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one-step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance. Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning. Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal. We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal.

Model-based deep reinforcement learning (DRL) algorithm uses the sampled data from a real environment to learn the underlying system dynamics to construct an approximate cyber environment. By using the synthesized data generated from the cyber environment to train the target controller, the training cost can be reduced significantly. In current research, issues such as the applicability of approximate model and the strategy to sample and train from the real and cyber environment have not been fully investigated. To address these issues, we propose to utilize an intelligent trainer to properly use the approximate model and control the sampling and training procedure in the model-based DRL. To do so, we package the training process of a model-based DRL as a standard RL environment, and design an RL trainer to control the training process. The trainer has three control actions: the first action controls where to sample in the real and cyber environment; the second action determines how many data should be sampled from the cyber environment and the third action controls how many times the cyber data should be used to train the target controller. These actions would be controlled manually if without the trainer. The proposed framework is evaluated on five different tasks of OpenAI gym and the test results show that the proposed trainer achieves significant better performance than a fixed parameter model-based RL baseline algorithm. In addition, we compare the performance of the intelligent trainer to a random trainer and prove that the intelligent trainer can indeed learn on the fly. The proposed training framework can be extended to more control actions with more sophisticated trainer design to further reduce the tweak cost of model-based RL algorithms.

We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU optimizes over the proximal policy space to find a non-parameterized policy. It then solves a supervised regression problem to convert the non-parameterized policy to a parameterized policy, from which it draws new samples. There is significant flexibility in setting the labels in the supervised regression problem, with different settings corresponding to different underlying optimization problems. We develop a methodology for finding an optimal policy in the non-parameterized policy space, and show how Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) can be addressed by this methodology. In terms of sample efficiency, our experiments show SPU can outperform PPO for simulated robotic locomotion tasks.

Reinforcement learning (RL) is a systematic approach to learning in sequential decision problems, where a learners' future task performance depends on its past actions. In such settings, learners have to explore, meaning they have to take actions with uncertain outcomes, to facilitate learning about the consequences of such actions. The question of how to best explore is a key open question in RL. Here, we specifically address this question from an empirical perspective, and investigate how to explore in a way that leads to sample efficient learning in deep RL, i.e., reinforcement learning with value function approximators that are parameterized as powerful neural networks. We present a surprising finding: in this setting, good approximate value functions can be learned without any explicit exploration. In fact, we find that in several cases learning without explicit exploration is equally or more sample efficient than the most-commonly used ɛ-greedy exploration scheme on several standard benchmark tasks. We present additional results that suggest a likely role of model structure (network depth and nonlinearity) in inducing such implicit exploration. We believe that our insights have strong practical implications and open up a novel line of research towards understanding exploration in deep RL.

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games. We identify three key challenges that any algorithm needs to master in order to perform well on all games: processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently. In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games. A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of $\gamma = 0.999$ (instead of $\gamma = 0.99$) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states. When tested on a set of 42 Atari games, our algorithm exceeds the performance of an average human on 40 games using a common set of hyper parameters. Furthermore, it is the first deep RL algorithm to solve the first level of Montezuma's Revenge.