Reinforcement learning has shown great promise in robotics thanks to its ability to develop efficient robotic control procedures through self-training. In particular, reinforcement learning has been successfully applied to solving the reaching task with robotic arms. In this paper, we define a robust, reproducible and systematic experimental procedure to compare the performance of various model-free algorithms at solving this task. The policies are trained in simulation and are then transferred to a physical robotic manipulator. It is shown that augmenting the reward signal with the Hindsight Experience Replay exploration technique increases the average return of off-policy agents between 7 and 9 folds when the target position is initialised randomly at the beginning of each episode.

Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a temporal dimension. In this paper, we address robotic musicianship by introducing a temporal extension to goal-conditioned reinforcement learning: Time-dependent goals. We demonstrate that these can be used to train a robotic musician to play the theremin instrument. We train the robotic agent in simulation and transfer the acquired policy to a real-world robotic thereminist. Supplemental video: https://youtu.be/jvC9mPzdQN4

Reinforcement learning has successfully learned to play challenging board and video games. However, its generalization ability remains under-explored. The General Video Game AI Learning Competition aims at designing agents that are capable of learning to play different games levels that were unseen during training. This paper presents the games, entries and results of the 2020 General Video Game AI Learning Competition, held at the Sixteenth International Conference on Parallel Problem Solving from Nature and the 2020 IEEE Conference on Games. Three new games with sparse, periodic and dense rewards, respectively, were designed for this competition and the test levels were generated by adding minor perturbations to training levels or combining training levels. In this paper, we also design a reinforcement learning agent, called Arcane, for general video game playing. We assume that it is more likely to observe similar local information in different levels rather than global information. Therefore, instead of directly inputting a single, raw pixel-based screenshot of current game screen, Arcane takes the encoded, transformed global and local observations of the game screen as two simultaneous inputs, aiming at learning local information for playing new levels. Two versions of Arcane, using a stochastic or deterministic policy for decision-making during test, both show robust performance on the game set of the 2020 General Video Game AI Learning Competition.

This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning. A custom simulator was developed in order to experimentally investigate the navigation problem of 4 cooperative non-holonomic robots sharing limited state information with each other in 3 different settings. The notion of decentralized motion planning with common and shared policy learning was adopted, which allowed robust training and testing of this approach in a stochastic environment since the agents were mutually independent and exhibited asynchronous motion behavior. The task was further aggravated by providing the agents with a sparse observation space and requiring them to generate continuous action commands so as to efficiently, yet safely navigate to their respective goal locations, while avoiding collisions with other dynamic peers and static obstacles at all times. The experimental results are reported in terms of quantitative measures and qualitative remarks for both training and deployment phases.

Inverse reinforcement learning (IRL) is a common technique for inferring human preferences from data. Standard IRL techniques tend to assume that the human demonstrator is stationary, that is that their policy $\pi$ doesn't change over time. In practice, humans interacting with a novel environment or performing well on a novel task will change their demonstrations as they learn more about the environment or task. We investigate the consequences of relaxing this assumption of stationarity, in particular by modelling the human as learning. Surprisingly, we find in some small examples that this can lead to better inference than if the human was stationary. That is, by observing a demonstrator who is themselves learning, a machine can infer more than by observing a demonstrator who is noisily rational. In addition, we find evidence that misspecification can lead to poor inference, suggesting that modelling human learning is important, especially when the human is facing an unfamiliar environment.

Reinforcement learning (RL) has recently shown impressive success in various computer games and simulations. Most of these successes are based on numerous episodes to be learned from. For typical robotic applications, however, the number of feasible attempts is very limited. In this paper we present a sample-efficient RL algorithm applied to the example of a table tennis robot. In table tennis every stroke is different, of varying placement, speed and spin. Therefore, an accurate return has be found depending on a high-dimensional continuous state space. To make learning in few trials possible the method is embedded into our robot system. In this way we can use a one-step environment. The state space depends on the ball at hitting time (position, velocity, spin) and the action is the racket state (orientation, velocity) at hitting. An actor-critic based deterministic policy gradient algorithm was developed for accelerated learning. Our approach shows competitive performance in both simulation and on the real robot in different challenging scenarios. Accurate results are always obtained within under 200 episodes of training. The video presenting our experiments is available at https://youtu.be/uRAtdoL6Wpw.

Two timescale stochastic approximation (SA) has been widely used in value-based reinforcement learning algorithms. In the policy evaluation setting, it can model the linear and nonlinear temporal difference learning with gradient correction (TDC) algorithms as linear SA and nonlinear SA, respectively. In the policy optimization setting, two timescale nonlinear SA can also model the greedy gradient-Q (Greedy-GQ) algorithm. In previous studies, the non-asymptotic analysis of linear TDC and Greedy-GQ has been studied in the Markovian setting, with diminishing or accuracy-dependent stepsize. For the nonlinear TDC algorithm, only the asymptotic convergence has been established. In this paper, we study the non-asymptotic convergence rate of two timescale linear and nonlinear TDC and Greedy-GQ under Markovian sampling and with accuracy-independent constant stepsize. For linear TDC, we provide a novel non-asymptotic analysis and show that it attains an $\epsilon$-accurate solution with the optimal sample complexity of $\mathcal{O}(\epsilon^{-1}\log(1/\epsilon))$ under a constant stepsize. For nonlinear TDC and Greedy-GQ, we show that both algorithms attain $\epsilon$-accurate stationary solution with sample complexity $\mathcal{O}(\epsilon^{-2})$. It is the first non-asymptotic convergence result established for nonlinear TDC under Markovian sampling and our result for Greedy-GQ outperforms the previous result orderwisely by a factor of $\mathcal{O}(\epsilon^{-1}\log(1/\epsilon))$.

One important goal in sequential decision making problems is to construct a policy that maximizes the average reward over a certain amount of the time. Depending on the purpose of applications, the duration of the learned policy for use in the future (i.e., the planning horizon) is often unknown and can be different from what we consider in the stage of policy optimization. In addition, the performance measure used in learning the policy often depends on the choice of the initial state's distribution. It is always of a great interest to learn a policy with strong generalizability and adaptivity. Given a pre-collected data of multiple trajectories consisting of states, actions and rewards, our goal is to learn a robust policy in the sense that it can guarantee the uniform performance over the unknown planning horizon and the distributional change in the initial state.

We study the offline policy learning problem in a contextual bandit framework. Specifically, we focus on the issue of overfitting which is especially important in a modern context where we often use overparameterized models that can interpolate the data. Our first contribution is to introduce a regret decomposition into approximation, estimation, and bandit errors that emphasizes the distinction between the policy learning and supervised learning problems. The bandit error measures the error from overfitting to the single action observed at each context, which we call "bandit overfitting". Our second contribution is to show both in theory and experiments how bandit overfitting is different for policy-based versus value-based algorithms when we use overparameterized models. We find that bandit overfitting can become a severe problem for policy-based algorithms, but value-based algorithms effectively reduce the policy learning problem to regression and thus avoid the worst problems of bandit overfitting.

Recent research on structured exploration placed emphasis on identifying novel states in the state space and incentivizing the agent to revisit them through intrinsic reward bonuses. In this study, we question whether the performance boost demonstrated through these methods is indeed due to the discovery of structure in exploratory schedule of the agent or is the benefit largely attributed to the perturbations in the policy and reward space manifested in pursuit of structured exploration. In this study we investigate the effect of perturbations in policy and reward spaces on the exploratory behavior of the agent. We proceed to show that simple acts of perturbing the policy just before the softmax layer and introduction of sporadic reward bonuses into the domain can greatly enhance exploration in several domains of the arcade learning environment. In light of these findings, we recommend benchmarking any enhancements to structured exploration research against the backdrop of noisy exploration.