Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm. Sequential modeling, however, requires capturing accurate dynamics across long horizons in trajectory data to ensure reasonable policy performance. To meet this requirement, leveraging large, expressive models has become a popular choice in recent literature, which, however, comes at the cost of significantly increased computation and inference latency. Contradictory yet promising, we reveal that lightweight models as simple as shallow 2-layer MLPs, can also enjoy accurate dynamics consistency and significantly reduced sequential modeling errors against large expressive models by adopting a simple recursive planning scheme: recursively planning coarse-grained future sub-goals based on current and target information, and then executes the action with a goal-conditioned policy learned from data relabeled with these sub-goal ground truths. We term our method as Recursive Skip-Step Planning (RSP). Simple yet effective, RSP enjoys great efficiency improvements thanks to its lightweight structure, and substantially outperforms existing methods, reaching new SOTA performances on the D4RL benchmark, especially in multi-stage long-horizon tasks.

Offline reinforcement learning (RL) enables learning policies from precollected datasets without online data collection. Although it offers the possibility to surpass the performance of the datasets, most existing offline RL algorithms struggle to compete with behavior cloning policies in many dataset settings due to trading off policy improvement and additional regularization to address the distributional shift issue. In many cases, if one can imitate a sequence of suboptimal subtrajectories in data and properly“stitch”them toward reaching an ideal future state, it may potentially result in a more reliable policy while avoiding difficulties that present in typical value-based offline RL algorithms. We borrow the idea of curriculum learning to embody the above intuition. We construct a curriculum that progressively imitates a sequence of suboptimal trajectories conditioned on a series of carefully constructed future states and cumulative rewards as goals. The suboptimal trajectories gradually guide policy learning toward reaching the ideal goal states. We name our algorithm curriculum goal-conditioned imitation (CGI). Experimental results show that CGI achieves competitive performance against state-of-the-art offline RL algorithms, especially for challenging tasks with long horizons and sparse rewards.

Beamforming plays an important role in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications. Optimizing beamforming configurations for 5G base stations (BSs) can substantially improve the quality of service for mobile users, and thus has great practical value. However, identifying the optimal beamforming configurations has proven to be a complex task, and is even more challenging when accounting for the unavoidable coupled influence among multiple densely deployed 5G BSs. In this paper, we propose a highly efficient deep multi-agent Bayesian optimization framework for the coordinated beamforming optimization problem that involves multiple BSs. Its core algorithm is built upon a deep ensemble of neural networks and a sample-efficient upper confidence bound (UCB) based exploration strategy. Our numerical results show that the proposed approach is highly effective in searching for optimally coordinated beamforming vectors under extremely large search space, and beats strong multi-agent reinforcement learning baselines in terms of optimization quality and sample efficiency.

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability. Code is available at: https://github.com/pcheng2/TSRL

Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution generalization but suffer from erroneous off-policy evaluation. Imitation-based methods avoid off-policy evaluation but are too conservative to surpass the dataset. In this study, we propose an alternative approach, inheriting the training stability of imitation-style methods while still allowing logical out-of-distribution generalization. We decompose the conventional reward-maximizing policy in offline RL into a guide-policy and an execute-policy. During training, the guide-poicy and execute-policy are learned using only data from the dataset, in a supervised and decoupled manner. During evaluation, the guide-policy guides the execute-policy by telling where it should go so that the reward can be maximized, serving as the \textit{Prophet}. By doing so, our algorithm allows \textit{state-compositionality} from the dataset, rather than \textit{action-compositionality} conducted in prior imitation-style methods. We dumb this new approach Policy-guided Offline RL (\texttt{POR}). \texttt{POR} demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline RL. We also highlight the benefits of \texttt{POR} in terms of improving with supplementary suboptimal data and easily adapting to new tasks by only changing the guide-poicy.