Minimum Bayes Risk (MBR) decoding optimizes output selection by maximizing the expected utility value of an underlying human distribution. While prior work has shown the effectiveness of MBR decoding through empirical evaluation, few studies have analytically investigated why the method is effective. As a result of our analysis, we show that, given the size $n$ of the reference hypothesis set used in computation, MBR decoding approaches the optimal solution with high probability at a rate of $O\left(n^{-\frac{1}{2}}\right)$, under certain assumptions, even though the language space $Y$ is significantly larger $Y\gg n$. This result helps to theoretically explain the strong performance observed in several prior empirical studies on MBR decoding. In addition, we provide the performance gap for maximum-a-posteriori (MAP) decoding and compare it to MBR decoding. The result of this paper indicates that MBR decoding tends to converge to the optimal solution faster than MAP decoding in several cases.

Best-of-N (BoN) sampling with a reward model has been shown to be an effective strategy for aligning Large Language Models (LLMs) with human preferences at the time of decoding. BoN sampling is susceptible to a problem known as reward hacking. Since the reward model is an imperfect proxy for the true objective, an excessive focus on optimizing its value can lead to a compromise of its performance on the true objective. Previous work proposes Regularized BoN sampling (RBoN), a BoN sampling with regularization to the objective, and shows that it outperforms BoN sampling so that it mitigates reward hacking and empirically (Jinnai et al., 2024). However, Jinnai et al. (2024) introduce RBoN based on a heuristic and they lack the analysis of why such regularization strategy improves the performance of BoN sampling. The aim of this study is to analyze the effect of BoN sampling on regularization strategies. Using the regularization strategies corresponds to robust optimization, which maximizes the worst case over a set of possible perturbations in the proxy reward. Although the theoretical guarantees are not directly applicable to RBoN, RBoN corresponds to a practical implementation. This paper proposes an extension of the RBoN framework, called Stochastic RBoN sampling (SRBoN), which is a theoretically guaranteed approach to worst-case RBoN in proxy reward. We then perform an empirical evaluation using the AlpacaFarm and Anthropic's hh-rlhf datasets to evaluate which factors of the regularization strategies contribute to the improvement of the true proxy reward. In addition, we also propose another simple RBoN method, the Sentence Length Regularized BoN, which has a better performance in the experiment as compared to the previous methods.

This study proposes an approach to human-to-humanoid teleoperation using GAN-based online motion retargeting, which obviates the need for the construction of pairwise datasets to identify the relationship between the human and the humanoid kinematics. Consequently, it can be anticipated that our proposed teleoperation system will reduce the complexity and setup requirements typically associated with humanoid controllers, thereby facilitating the development of more accessible and intuitive teleoperation systems for users without robotics knowledge. The experiments demonstrated the efficacy of the proposed method in retargeting a range of upper-body human motions to humanoid, including a body jab motion and a basketball shoot motion. Moreover, the human-in-the-loop teleoperation performance was evaluated by measuring the end-effector position errors between the human and the retargeted humanoid motions. The results demonstrated that the error was comparable to those of conventional motion retargeting methods that require pairwise motion datasets. Finally, a box pick-and-place task was conducted to demonstrate the usability of the developed humanoid teleoperation system.

We introduce the ``soft Deep MaxPain'' (softDMP) algorithm, which integrates the optimization of long-term policy entropy into reward-punishment reinforcement learning objectives. Our motivation is to facilitate a smoother variation of operators utilized in the updating of action values beyond traditional ``max'' and ``min'' operators, where the goal is enhancing sample efficiency and robustness. We also address two unresolved issues from the previous Deep MaxPain method. Firstly, we investigate how the negated (``flipped'') pain-seeking sub-policy, derived from the punishment action value, collaborates with the ``min'' operator to effectively learn the punishment module and how softDMP's smooth learning operator provides insights into the ``flipping'' trick. Secondly, we tackle the challenge of data collection for learning the punishment module to mitigate inconsistencies arising from the involvement of the ``flipped'' sub-policy (pain-avoidance sub-policy) in the unified behavior policy. We empirically explore the first issue in two discrete Markov Decision Process (MDP) environments, elucidating the crucial advancements of the DMP approach and the necessity for soft treatments on the hard operators. For the second issue, we propose a probabilistic classifier based on the ratio of the pain-seeking sub-policy to the sum of the pain-seeking and goal-reaching sub-policies. This classifier assigns roll-outs to separate replay buffers for updating reward and punishment action-value functions, respectively. Our framework demonstrates superior performance in Turtlebot 3's maze navigation tasks under the ROS Gazebo simulation.

Approaches based on generative adversarial networks for imitation learning are promising because they are sample efficient in terms of expert demonstrations. However, training a generator requires many interactions with the actual environment because model-free reinforcement learning is adopted to update a policy. To improve the sample efficiency using model-based reinforcement learning, we propose model-based Entropy-Regularized Imitation Learning (MB-ERIL) under the entropy-regularized Markov decision process to reduce the number of interactions with the actual environment. MB-ERIL uses two discriminators. A policy discriminator distinguishes the actions generated by a robot from expert ones, and a model discriminator distinguishes the counterfactual state transitions generated by the model from the actual ones. We derive structured discriminators so that the learning of the policy and the model is efficient. Computer simulations and real robot experiments show that MB-ERIL achieves a competitive performance and significantly improves the sample efficiency compared to baseline methods.

This paper proposes Entropy-Regularized Imitation Learning (ERIL), which is a combination of forward and inverse reinforcement learning under the framework of the entropy-regularized Markov decision process. ERIL minimizes the reverse Kullback-Leibler (KL) divergence between two probability distributions induced by a learner and an expert. Inverse reinforcement learning (RL) in ERIL evaluates the log-ratio between two distributions using the density ratio trick, which is widely used in generative adversarial networks. More specifically, the log-ratio is estimated by building two binary discriminators. The first discriminator is a state-only function, and it tries to distinguish the state generated by the forward RL step from the expert's state. The second discriminator is a function of current state, action, and transitioned state, and it distinguishes the generated experiences from the ones provided by the expert. Since the second discriminator has the same hyperparameters of the forward RL step, it can be used to control the discriminator's ability. The forward RL minimizes the reverse KL estimated by the inverse RL. We show that minimizing the reverse KL divergence is equivalent to finding an optimal policy under entropy regularization. Consequently, a new policy is derived from an algorithm that resembles Dynamic Policy Programming and Soft Actor-Critic. Our experimental results on MuJoCo-simulated environments show that ERIL is more sample-efficient than such previous methods. We further apply the method to human behaviors in performing a pole-balancing task and show that the estimated reward functions show how every subject achieves the goal.

Policy gradient Reinforcement Learning (RL) algorithms have received substantial attention,seeking stochastic policies that maximize the average (or discounted cumulative) reward. In addition, extensions based on the concept of the Natural Gradient (NG) show promising learning efficiency because these regard metrics for the task. Though there are two candidate metrics, Kakade's Fisher Information Matrix (FIM) for the policy (action) distribution and Morimura's FIM for the stateaction jointdistribution, but all RL algorithms with NG have followed Kakade's approach. In this paper, we describe a generalized Natural Gradient (gNG) that linearly interpolates the two FIMs and propose an efficient implementation for the gNG learning based on a theory of the estimating function, the generalized Natural Actor-Critic(gNAC) algorithm. The gNAC algorithm involves a near optimal auxiliary function to reduce the variance of the gNG estimates. Interestingly, the gNAC can be regarded as a natural extension of the current state-of-the-art NAC algorithm [1], as long as the interpolating parameter is appropriately selected. Numerical experimentsshowed that the proposed gNAC algorithm can estimate gNG efficiently and outperformed the NAC algorithm.

This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. The most significant features of our team, TRACKIES, are the application of a reinforcement learning method enhanced for real robot applications and the use of an omnidirectional vision system for our goalie that can capture a 360-degree view at any instant in time. The method and the system used are shown with competition results.

This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. The most significant features of our team, TRACKIES, are the application of a reinforcement learning method enhanced for real robot applications and the use of an omnidirectional vision system for our goalie that can capture a 360-degree view at any instant in time. The method and the system used are shown with competition results.