Stepwise Alignment for Constrained Language Model Policy Optimization

Safety and trustworthiness are indispensable requirements for applying AI systems based on large language models (LLMs) in real-world applications. This paper formulates a human value alignment as a language model policy optimization problem to maximize reward under a safety constraint and then proposes an algorithm called Stepwise Alignment for Constrained Policy Optimization (SACPO). A key idea behind SACPO, supported by theory, is that the optimal policy incorporating both reward and safety can be directly obtained from a reward-aligned policy. Based on this key idea, SACPO aligns the LLMs with each metric step-wise while leveraging simple yet powerful alignment algorithms such as direct preference optimization (DPO). SACPO provides many benefits such as simplicity, stability, computational efficiency, and flexibility regarding algorithms and dataset selection. Under mild assumption, our theoretical analysis provides the upper bounds regarding near-optimality and safety constraint violation. Our experimental results show that SACPO can fine-tune Alpaca-7B better than the state-of-the-art method in terms of both helpfulness and harmlessness

Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification

In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to the corresponding real-world environment. However, the simulation environment does not perfectly mimic the real-world environment, lead to model misspecification. Multiple studies report significant deterioration of policy performance in a real-world environment. In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. To obtain a policy for the optimization, we propose an off-policy actor-critic approach called the Max-Min Twin Delayed Deep Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min optimization problem using a simultaneous gradient ascent descent approach. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.

Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution

Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural.