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1. Latent Action Space for Offline Reinforcement Learning

   Zhou, Wenxuan; Bajracharya, Sujay; Held, David (January 2020, Conference on Robot Learning)

   null (Ed.)

   The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more important paradigm for real-world applications of reinforcement learning such as robotics, in which data collection is slow and potentially dangerous. Existing off-policy algorithms have limited performance on static datasets due to extrapolation errors from out-of-distribution actions. This leads to the challenge of constraining the policy to select actions within the support of the dataset during training. We propose to simply learn the Policy in the Latent Action Space (PLAS) such that this requirement is naturally satisfied. We evaluate our method on continuous control benchmarks in simulation and a deformable object manipulation task with a physical robot. We demonstrate that our method provides competitive performance consistently across various continuous control tasks and different types of datasets, outperforming existing offline reinforcement learning methods with explicit constraints. 

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2. Minimax Model Learning

   Voloshin, C; Jiang, N; Yue, YS (January 2021, 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS))

   Banerjee, A; Fukumizu, K (Ed.)

   We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning. Notably, our loss is derived from the off-policy policy evaluation objective with an emphasis on correcting distribution shift. Compared to previous model-based techniques, our approach allows for greater robustness under model mis-specification or distribution shift induced by learning/evaluating policies that are distinct from the data-generating policy. We provide a theoretical analysis and show empirical improvements over existing model-based off-policy evaluation methods. We provide further analysis showing our loss can be used for off-policy optimization (OPO) and demonstrate its integration with more recent improvements in OPO. 

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3. Data Efficient Learning of Robust Control Policies

   https://doi.org/10.1109/ALLERTON.2018.8636072  

   Jhal, Susmit; Lincoln, Patrick (October 2018, 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton))

   This paper investigates data-efficient methods for learning robust control policies. Reinforcement learning has emerged as an effective approach to learn control policies by interacting directly with the plant, but it requires a significant number of example trajectories to converge to the optimal policy. Combining model-free reinforcement learning with model-based control methods achieves better data-efficiency via simultaneous system identification and controller synthesis. We study a novel approach that exploits the existence of approximate physics models to accelerate the learning of control policies. The proposed approach consists of iterating through three key steps: evaluating a selected policy on the real-world plant and recording trajectories, building a Gaussian process model to predict the reality-gap of a parametric physics model in the neighborhood of the selected policy, and synthesizing a new policy using reinforcement learning on the refined physics model that most likely approximates the real plant. The approach converges to an optimal policy as well as an approximate physics model. The real world experiments are limited to evaluating only promising candidate policies, and the use of Gaussian processes minimizes the number of required real world trajectories. We demonstrate the effectiveness of our techniques on a set of simulation case-studies using OpenAI gym environments. 

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4. Imitation-Projected Programmatic Reinforcement Learning

   Verma, A (December 2019, Advances in neural information processing systems)

   Wallach, H (Ed.)

   We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such policies remains a challenge. Our approach to this challenge-a meta-algorithm called PROPEL-is based on three insights. First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space. Second, we view the unconstrained policy space as mixing neural and programmatic representations, which enables employing state-of-the-art deep policy gradient approaches. Third, we cast the projection step as program synthesis via imitation learning, and exploit contemporary combinatorial methods for this task. We present theoretical convergence results for PROPEL and empirically evaluate the approach in three continuous control domains. The experiments show that PROPEL can significantly outperform state-of-the-art approaches for learning programmatic policies. 

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5. Leveraging Fully Observable Policies for Learning under Partial Observability

   Nguyen, Hai Huu; Baisero, Andrea; Wang, Dian; Amato, Christopher; Platt, Robert (January 2023, Conference on Robot Learning)

   Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state information is often possible. In particular, we propose a method for partially observable reinforcement learning that uses a fully observable policy (which we call a \emph{state expert}) during training to improve performance. Based on Soft Actor-Critic (SAC), our agent balances performing actions similar to the state expert and getting high returns under partial observability. Our approach can leverage the fully-observable policy for exploration and parts of the domain that are fully observable while still being able to learn under partial observability. On six robotics domains, our method outperforms pure imitation, pure reinforcement learning, the sequential or parallel combination of both types, and a recent state-of-the-art method in the same setting. A successful policy transfer to a physical robot in a manipulation task from pixels shows our approach's practicality in learning interesting policies under partial observability. 

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