It is still a challenging topic to make reactive driving behaviors in complex urban environments as road users' intentions are unknown. Model-based reinforcement learning (MBRL) offers great potential to learn a reactive policy by constructing a world model that can provide informative states and imagination training. However, a critical limitation in relevant research lies in the scene-level reconstruction representation learning, which may overlook key interactive vehicles and hardly model the interactive features among vehicles and their long-term intentions. Therefore, this paper presents a novel MBRL method with a predictive individual world model (PIWM) for autonomous driving. PIWM describes the driving environment from an individual-level perspective and captures vehicles' interactive relations and their intentions via trajectory prediction task. Meanwhile, a behavior policy is learned jointly with PIWM. It is trained in PIWM's imagination and effectively navigates in the urban driving scenes leveraging intention-aware latent states. The proposed method is trained and evaluated on simulation environments built upon real-world challenging interactive scenarios. Compared with popular model-free and state-of-the-art model-based reinforcement learning methods, experimental results show that the proposed method achieves the best performance in terms of safety and efficiency.

Update: I have read the author response and am satisfied with the commitment to elaborate on \beta and \pi and to simplify the Stan PE code with a "pseudo-extended" function. This paper presents a new MCMC sampling method called pseudo-extended MCMC that uses an instrumental distribution to projects the data into a higher-dimensional space where the modes are connected, making it easier for the sampler to mix. A default instrumental distribution based on tempering is provided. The method is compared to existing baselines showing efficacy on three benchmark datasets. The paper is well-placed within the existing literature.

Reviewers reached consensus that the paper makes a valuable contribution for MCMC. There are specific suggestions for improving the experiments that we ask the authors to seriously consider.

Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carlo (MCMC) are used, the end result incurs costs to both privacy and accuracy. Existing work has examined these effects asymptotically, but implementable finite sample results are needed in practice so that users can specify privacy budgets in advance and implement samplers with exact privacy guarantees. In this paper, we use tools from ergodic theory and perfect simulation to design exact finite runtime sampling algorithms for the exponential mechanism by introducing an intermediate modified target distribution using artificial atoms. We propose an additional modification of this sampling algorithm that maintains its \epsilon -DP guarantee and has improved runtime at the cost of some utility.

Summary and Contributions: The paper presents the concept of shared equilibrium in certain kinds of multi agent stochastic games with a restricted form of partial observability. The formalism includes the notion of supertypes (different distributions of agents) and types (where each agents is given a true type each episode). The agent's type influences the rewards available as does the joint state of the system and joint action over all agents. One key constraint is that all agents of the same type follow the same policy from an egocentric perspective (where they themselves are the focal agent and all other agents are interchangeable). They define a policy gradient approach for individual agents, also present a higher order learning rule that shifts the distribution over supertypes at a slower timescale.

The paper was refereed by 4 knowledgeable reviewers. All reviewers appreciated the contributions of the paper: - Formalization of self play and formal proof when it is guaranteed to converge - New algorithm for calibrating equilibria that is more effective than a naive use of BO. - Convincing results on a market agent scenario. The biggest concern that was discussed between the reviewers was the assumption of the extended transitivity. While this was addressed partially in the rebuttal, the authors should add a longer discussion in the paper for which games this assumption holds. However, after the discussion all reviewers agreed that the paper merits acceptance and I join this decision.

Considering the continuous-time Mean-Variance (MV) portfolio optimization problem, we study a regime-switching market setting and apply reinforcement learning (RL) techniques to assist informed exploration within the control space. We introduce and solve the Exploratory Mean Variance with Regime Switching (EMVRS) problem. We also present a Policy Improvement Theorem. Further, we recognize that the widely applied Temporal Difference (TD) learning is not adequate for the EMVRS context, hence we consider Orthogonality Condition (OC) learning, leveraging the martingale property of the induced optimal value function from the analytical solution to EMVRS. We design a RL algorithm that has more meaningful parameterization using the market parameters and propose an updating scheme for each parameter. Our empirical results demonstrate the superiority of OC learning over TD learning with a clear convergence of the market parameters towards their corresponding ``grounding true" values in a simulated market scenario. In a real market data study, EMVRS with OC learning outperforms its counterparts with the highest mean and reasonably low volatility of the annualized portfolio returns.

We consider the inverse reinforcement learning (IRL) problem, where an unknown reward function of some Markov decision process is estimated based on observed expert demonstrations. In most existing approaches, IRL is formulated and solved as a nonconvex optimization problem, posing challenges in scenarios where robustness and reproducibility are critical. We discuss a convex formulation of the IRL problem (CIRL) initially proposed by Ng and Russel, and reformulate the problem such that the domain-specific language CVXPY can be applied directly to specify and solve the convex problem. We also extend the CIRL problem to scenarios where the expert policy is not given analytically but by trajectory as state-action pairs, which can be strongly inconsistent with optimality, by augmenting some of the constraints. Theoretical analysis and practical implementation for hyperparameter auto-selection are introduced. This note helps the users to easily apply CIRL for their problems, without background knowledge on convex optimization.

Summary and Contributions: Post-rebuttal: I would like to thank the authors for the thoughtful response. The main issue for me was clarity, and I'm happy that the authors agreed to improve this aspect of the paper. However, it's hard to increase my score based on this promise alone. Nevertheless, my recommendation should really be considered a borderline recommendation. I will not fight against accepting this paper. This involves both filtering and control.

The paper describes new offline and online techniques to optimize the policy of continuous time discrete state and action POMDPs. This paper makes an important contribution to the RL and control literature. Very little work has focused on continuous time control problems in the ML community. While the techniques assume that the model is known, do not scale to high dimensional problems and were tested only on toy problems, they introduce new formalisms that will help the community get familiar with the mathematics of continuous time control. Hence this paper will be of high interest for the RL community.