Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks. However, previous recurrent RL methods face training stability issues due to the gradient instability of RNNs. In this paper, we propose Recurrent Off-policy RL with Context-Encoder-Specific Learning Rate (RESeL) to tackle this issue. Specifically, RESeL uses a lower learning rate for context encoder than other MLP layers to ensure the stability of the former while maintaining the training efficiency of the latter. We integrate this technique into existing off-policy RL methods, resulting in the RESeL algorithm. We evaluated RESeL in 18 POMDP tasks, including classic, meta-RL, and credit assignment scenarios, as well as five MDP locomotion tasks. The experiments demonstrate significant improvements in training stability with RESeL. Comparative results show that RESeL achieves notable performance improvements over previous recurrent RL baselines in POMDP tasks, and is competitive with or even surpasses state-of-the-art methods in MDP tasks. Further ablation studies highlight the necessity of applying a distinct learning rate for the context encoder.

Thanks to their simple architecture, Restricted Boltzmann Machines (RBMs) are powerful tools for modeling complex systems and extracting interpretable insights from data. However, training RBMs, as other energy-based models, on highly structured data poses a major challenge, as effective training relies on mixing the Markov chain Monte Carlo simulations used to estimate the gradient. This process is often hindered by multiple second-order phase transitions and the associated critical slowdown. In this paper, we present an innovative method in which the principal directions of the dataset are integrated into a low-rank RBM through a convex optimization procedure. This approach enables efficient sampling of the equilibrium measure via a static Monte Carlo process. By starting the standard training process with a model that already accurately represents the main modes of the data, we bypass the initial phase transitions. Our results show that this strategy successfully trains RBMs to capture the full diversity of data in datasets where previous methods fail. Furthermore, we use the training trajectories to propose a new sampling method, {\em parallel trajectory tempering}, which allows us to sample the equilibrium measure of the trained model much faster than previous optimized MCMC approaches and a better estimation of the log-likelihood. We illustrate the success of the training method on several highly structured datasets.

We need to look at our shoelaces as we first learn to tie them but having mastered this skill, we can do it from touch alone. We call this phenomenon "sensory scaffolding": observation streams that are not needed by a master might yet aid a novice learner. We consider such sensory scaffolding setups for training artificial agents. For example, a robot arm may need to be deployed with just a low-cost, robust, general-purpose camera; yet its performance may improve by having privileged training-time-only access to informative albeit expensive and unwieldy motion capture rigs or fragile tactile sensors. For these settings, we propose Scaffolder, a reinforcement learning approach which effectively exploits privileged sensing in critics, world models, reward estimators, and other such auxiliary components that are only used at training time, to improve the target policy. For evaluating sensory scaffolding agents, we design a new "S3" suite of ten diverse simulated robotic tasks that explore a wide range of practical sensor setups. Agents must use privileged camera sensing to train blind hurdlers, privileged active visual perception to help robot arms overcome visual occlusions, privileged touch sensors to train robot hands, and more. Scaffolder easily outperforms relevant prior baselines and frequently performs comparably even to policies that have test-time access to the privileged sensors. It is well-known that Beethoven composed symphonies long after he had fully lost his hearing. Such feats are commonly held to be evidence of mastery: for example, novice typists need to look at the keyboard to locate keys but with practice, can graduate to typing without looking. Thus, sensing requirements may be different during learning versus after learning. We refer to this as "sensory scaffolding", drawing inspiration from the concept of scaffolding teaching mechanisms in psychology that provide temporary support for a student (Wood et al., 1976; Vygotsky et al., 2011), like training wheels when learning to ride a bicycle. For artificial learning agents such as robots, sensory scaffolding permits decoupling the observation streams required at test time from those that are used to train the agent. The sensors available in a deployed robot are often decided by practical considerations such as cost, robustness, size, compute requirements, and ease of instrumentation, e.g., autonomous cars with only cheap and robust RGB camera sensors. However, those considerations might carry less weight at training time, so a robot learning practitioner may choose to scaffold policy learning with privileged information (Vapnik & Vashist, 2009) from extra sensors available only at training. In the case of the cars above, the manufacturer might equip a small fleet of training cars with expensive privileged sensors like lidar to improve RGB-only driving policies for customers to install in their cars.

The study of behavioral diversity in Multi-Agent Reinforcement Learning (MARL) is a nascent yet promising field. In this context, the present work deals with the question of how to control the diversity of a multi-agent system. With no existing approaches to control diversity to a set value, current solutions focus on blindly promoting it via intrinsic rewards or additional loss functions, effectively changing the learning objective and lacking a principled measure for it. To address this, we introduce Diversity Control (DiCo), a method able to control diversity to an exact value of a given metric by representing policies as the sum of a parameter-shared component and dynamically scaled per-agent components. By applying constraints directly to the policy architecture, DiCo leaves the learning objective unchanged, enabling its applicability to any actor-critic MARL algorithm. We theoretically prove that DiCo achieves the desired diversity, and we provide several experiments, both in cooperative and competitive tasks, that show how DiCo can be employed as a novel paradigm to increase performance and sample efficiency in MARL. Multimedia results are available on the paper's website: https://sites.google.com/view/dico-marl.

Intelligent Cyber-Physical Systems (ICPS) represent a specialized form of Cyber-Physical System (CPS) that incorporates intelligent components, notably Convolutional Neural Networks (CNNs) and Deep Reinforcement Learning (DRL), to undertake multifaceted tasks encompassing perception, decision-making, and control. The utilization of DRL for decision-making facilitates dynamic interaction with the environment, generating control actions aimed at maximizing cumulative rewards. Nevertheless, the inherent uncertainty of the operational environment and the intricate nature of ICPS necessitate exploration within complex and dynamic state spaces during the learning phase. DRL confronts challenges in terms of efficiency, generalization capabilities, and data scarcity during decision-making process. In response to these challenges, we propose an innovative abstract modeling approach grounded in spatial-temporal value semantics, capturing the evolution in the distribution of semantic value across time and space. A semantics-based abstraction is introduced to construct an abstract Markov Decision Process (MDP) for the DRL learning process. Furthermore, optimization techniques for abstraction are delineated, aiming to refine the abstract model and mitigate semantic gaps between abstract and concrete states. The efficacy of the abstract modeling is assessed through the evaluation and analysis of the abstract MDP model using PRISM. A series of experiments are conducted, involving diverse scenarios such as lane-keeping, adaptive cruise control, and intersection crossroad assistance, to demonstrate the effectiveness of our abstracting approach.

Language models as intelligent agents push the boundaries of sequential decision-making agents but struggle with limited knowledge of environmental dynamics and exponentially huge action space. Recent efforts like GLAM and TWOSOME manually constrain the action space to a restricted subset and employ reinforcement learning to align agents' knowledge with specific environments. However, they overlook fine-grained credit assignments for intra-action tokens, which is essential for efficient language agent optimization, and rely on human's prior knowledge to restrict action space. This paper proposes decomposing language agent optimization from the action level to the token level, offering finer supervision for each intra-action token and manageable optimization complexity in environments with unrestricted action spaces. Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization. We then derive the Bellman backup with Action Decomposition (BAD) to integrate credit assignments for both intra-action and inter-action tokens, effectively eliminating the discrepancies. Implementing BAD within the PPO algorithm, we introduce Policy Optimization with Action Decomposition (POAD). POAD benefits from a finer-grained credit assignment process and lower optimization complexity, leading to enhanced learning efficiency and generalization abilities in aligning language agents with interactive environments. We validate POAD across diverse testbeds, with results affirming the advantages of our approach and the correctness of our theoretical analysis.

The dishonest casino is a well-known hidden Markov model (HMM) used in educational settings to introduce HMMs and graphical models. Here, a sequence of die rolls is observed, with the casino switching between a fair and a loaded die. Typically, the goal is to use the observed rolls to infer the pattern of fair and loaded dice, leading to filtering, smoothing, and Viterbi algorithms. This paper, however, explores how much of the winnings is attributable to the casino's cheating, a counterfactual question beyond the scope of HMM primitives. To address this, we introduce a structural causal model (SCM) consistent with the HMM and show that the expected winnings attributable to cheating (EWAC) can be bounded using linear programs (LPs). Through numerical experiments, we compute these bounds and develop intuition using benchmark SCMs based on independence, comonotonic, and counter-monotonic copulas. We show that tighter bounds are obtained with a time-homogeneity condition on the SCM, while looser bounds allow for an almost explicit LP solution. Domain-specific knowledge like pathwise monotonicity or counterfactual stability can be incorporated via linear constraints. Our work contributes to bounding counterfactuals in causal inference and is the first to develop LP bounds in a dynamic HMM setting, benefiting educational contexts where counterfactual inference is taught.

This study introduces a novel approach for learning mixtures of Markov chains, a critical process applicable to various fields, including healthcare and the analysis of web users. Existing research has identified a clear divide in methodologies for learning mixtures of discrete and continuous-time Markov chains, while the latter presents additional complexities for recovery accuracy and efficiency. We introduce a unifying strategy for learning mixtures of discrete and continuous-time Markov chains, focusing on hitting times, which are well defined for both types. Specifically, we design a reconstruction algorithm that outputs a mixture which accurately reflects the estimated hitting times and demonstrates resilience to noise. We introduce an efficient gradient-descent approach, specifically tailored to manage the computational complexity and non-symmetric characteristics inherent in the calculation of hitting time derivatives. Our approach is also of significant interest when applied to a single Markov chain, thus extending the methodologies previously established by Hoskins et al. and Wittmann et al. We complement our theoretical work with experiments conducted on synthetic and real-world datasets, providing a comprehensive evaluation of our methodology.

Creativity is already regularly attributed to AI systems outside specialised computational creativity (CC) communities. However, the evaluation of creativity in AI at large typically lacks grounding in creativity theory, which can promote inappropriate attributions and limit the analysis of creative behaviour. While CC researchers have translated psychological theory into formal models, the value of these models is limited by a gap to common AI frameworks. To mitigate this limitation, we identify formal mappings between Boden's process theory of creativity and Markov Decision Processes (MDPs), using the Creative Systems Framework as a stepping stone. We study three out of eleven mappings in detail to understand which types of creative processes, opportunities for (aberrations), and threats to creativity (uninspiration) could be observed in an MDP. We conclude by discussing quality criteria for the selection of such mappings for future work and applications.

Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with \emph{noisy} and \emph{incomplete} count data. Among these models, Poisson-Gamma Dynamical Systems (PGDSs) are proven to be effective in capturing the evolving dynamics underlying observed count sequences. However, the state-of-the-art PGDS still fails to capture the \emph{time-varying} transition dynamics that are commonly observed in real-world count time sequences. To mitigate this gap, a non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time, and the evolving transition matrices are modeled by sophisticatedly-designed Dirichlet Markov chains. Leveraging Dirichlet-Multinomial-Beta data augmentation techniques, a fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance due to its capacity to learn non-stationary dependency structure captured by the time-evolving transition matrices.