Dealing with real-world reinforcement learning (RL) tasks, we shall be aware that the environment may have sudden changes. We expect that a robust policy is able to handle such changes and adapt to the new environment rapidly. Context-based meta reinforcement learning aims at learning environment adaptable policies. These methods adopt a context encoder to perceive the environment on-the-fly, following which a contextual policy makes environment adaptive decisions according to the context. However, previous methods show lagged and unstable context extraction, which are hard to handle sudden changes well. This paper proposes an environment sensitive contextual policy learning (ESCP) approach, in order to improve both the sensitivity and the robustness of context encoding. ESCP is composed of three key components: variance minimization that forces a rapid and stable encoding of the environment context, relational matrix determinant maximization that avoids trivial solutions, and a history-truncated recurrent neural network model that avoids old memory interference. We use a grid-world task and 5 locomotion controlling tasks with changing parameters to empirically assess our algorithm. Experiment results show that in environments with both in-distribution and out-of-distribution parameter changes, ESCP can not only better recover the environment encoding, but also adapt more rapidly to the post-change environment (10x faster in the grid-world) while the return performance is kept or improved, compared with state-of-the-art meta RL methods.

Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge trial-and-error, making errors is always undesired in the real world. To improve the sample efficiency and thus reduce the errors, model-based reinforcement learning (MBRL) is believed to be a promising direction, which builds environment models in which the trial-and-errors can take place without real costs. In this survey, we take a review of MBRL with a focus on the recent progress in deep RL. For non-tabular environments, there is always a generalization error between the learned environment model and the real environment. As such, it is of great importance to analyze the discrepancy between policy training in the environment model and that in the real environment, which in turn guides the algorithm design for better model learning, model usage, and policy training. Besides, we also discuss the recent advances of model-based techniques in other forms of RL, including offline RL, goal-conditioned RL, multi-agent RL, and meta-RL. Moreover, we discuss the applicability and advantages of MBRL in real-world tasks. Finally, we end this survey by discussing the promising prospects for the future development of MBRL. We think that MBRL has great potential and advantages in real-world applications that were overlooked, and we hope this survey could attract more research on MBRL.

Imitation learning aims at recovering expert policies from limited demonstration data. Generative Adversarial Imitation Learning (GAIL) employs the generative adversarial learning framework for imitation learning and has shown great potentials. GAIL and its variants, however, are found highly sensitive to hyperparameters and hard to converge well in practice. One key issue is that the supervised learning discriminator has a much faster learning speed than the reinforcement learning generator, making the generator gradient vanishing. Although GAIL is formulated as a zero-sum adversarial game, the ultimate goal of GAIL is to learn the generator, thus the discriminator should play the role more like a teacher rather than a real opponent. Therefore, the learning of the discriminator should consider how the generator could learn. In this paper, we disclose that enhancing the gradient of the generator training is equivalent to increase the variance of the fake reward provided by the discriminator output. We thus propose an improved version of GAIL, GAIL-VR, in which the discriminator also learns to avoid generator gradient vanishing through regularization of the fake rewards variance. Experiments in various tasks, including locomotion tasks and Atari games, indicate that GAIL-VR can improve the training stability and imitation scores.

Inverse reinforcement learning (IRL) recovers the underlying reward function from expert demonstrations. A generalizable reward function is even desired as it captures the fundamental motivation of the expert. However, classical IRL methods can only recover reward functions coupled with the training dynamics, thus are hard to generalize to a changed environment. Previous dynamics-agnostic reward learning methods have strict assumptions, such as that the reward function has to be state-only. This work proposes a general approach to learn transferable reward functions, Dynamics-Agnostic Discriminator-Ensemble Reward Learning (DARL). Following the adversarial imitation learning (AIL) framework, DARL learns a dynamics-agnostic discriminator on a latent space mapped from the original state-action space. The latent space is learned to contain the least information of the dynamics. Moreover, to reduce the reliance of the discriminator on policies, the reward function is represented as an ensemble of the discriminators during training. We assess DARL in four MuJoCo tasks with dynamics transfer. Empirical results compared with the state-of-the-art AIL methods show that DARL can learn a reward that is more consistent with the true reward, thus obtaining higher environment returns.