Offline model-based reinforcement learning uses a model of the environment, learned from a static dataset of interactions, to guide policy generation. Sub-optimal planning decisions can be made when the agent explores states that are out-of-distribution, as the world model will have more uncertainty. This paper explores the use of pessimism, the tendency to avoid uncertain states, in the planning procedure. We evaluate Lower Confidence Bound, ensembles, and Monte Carlo dropout in the MinAtar breakout environment. Results indicate that ensemble methods yield the highest performance, with a significant performance gain over the baseline, while LCB also shows varying degrees of improvement. MC dropout is generally shown to not yield a performance improvement.

While model-free reinforcement learning (MFRL) approaches have been shown effective at solving a diverse range of environments, recent developments in model-based reinforcement learning (MBRL) have shown that it is possible to leverage its increased sample efficiency and generalisation abilities to solve highly complex tasks with fewer resources and environment interactions. The introduction of discrete latent states through categorical distributions allowed DreamerV2, a MBRL approach, to surpass the state-of-the-art MFRL Rainbow algorithm on the Arcade Learning Environment. Despite the successes of this approach, it is not yet understood why discretization improves performance. This paper investigates how the discretization of the latent space through categorical distribution affects planning performance in a deterministic environment. Further investigations are conducted on the model's generalization abilities and the impact of the latent space's shape on performance. By using a dataset of experiences instead of directly interacting with the environment, the models are trained in an offline setting. Results show that the discrete world model underperforms compared to a continuous latent space model while being significantly harder to train. Further investigations concluded that the number of categorical distributions has a high influence on performance and that in the considered setting the discrete world model can generalize better than the continuous baseline but it does so by sacrificing small gains in important metrics.

Traditionally, Recurrent Neural Networks (RNNs) are used to predict the sequential dynamics of the environment. With the advancement and breakthroughs of Transformer models, there has been demonstrated improvement in the performance & sample efficiency of Transformers as world models. The focus has been on partially-observable environments where their capabilities can be maximally utilised. In this paper, we sought to investigate the conditions under which transformers outperform RNNs given a fully observable environment where states obey the Markov property. This provides insight into transformers' generalisation and predictive capabilities. Specifically, our experiments explored the impact of model complexity and the size of the dataset. We observed that transformers did not outperform our baseline implementation when given up to 7000 episodes of trajectory data. It was also observed that having shorter sequence lengths had a negligible impact on the performance of the model, leading to our recommendation of avoiding using transformers in these fully observable environments.

We investigate the generalization performance of predictive models in model-based reinforcement learning when trained using maximum likelihood estimation (MLE) versus proper value equivalence (PVE) loss functions. While the more conventional MLE loss aims to fit models to predict state transitions and rewards as accurately as possible, value-equivalent methods (e.g. PVE) prioritize value-relevant features. We show that in a tabular setting, MLE-based models generalize better than their PVE counterparts when fit to a small number of training policies, whereas PVE-based models perform better as the number of policies increases. With increasing model rank, generalisation error tends to improve for MLE and PVE, and the two become closer in generalisation ability.

Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as Neural Netoworks) that are not guaranteed to converge to an optimal solution. As a consequence, the planning process using samples generated by an imperfect model is also not guaranteed to converge to the optimal policy. In fact, the mismatch between source and target dynamics distribution can result in compounding errors, leading to poor algorithm performance during testing. To mitigate this, we combine the Robust Markov Decision Processes (RMDPs) framework and an ensemble of models to take into account the uncertainty in the approximation of the dynamics. With RMDPs, we can study the uncertainty problem as a two-player stochastic game where Player 1 aims to maximize the expected return and Player 2 wants to minimize it. Using an ensemble of models, Player 2 can choose the worst model to carry out the transitions when performing rollout for the policy improvement. We experimentally show that finding a maximin strategy for this game results in a policy robust to model errors leading to better performance when compared to assuming the learned dynamics to be correct.

Previous research has in reinforcement learning for traffic control has used various state abstractions. Some use feature vectors while others use matrices of car positions. This paper first compares a simple feature vector consisting of only queue sizes per incoming lane to a matrix of car positions. Then it investigates if knowledge can be transferred from a simple agent using the feature vector abstraction to a more complex agent that uses the position matrix abstraction.We find that training cannot be sped up by first training an agent with the feature vector abstraction and then reusing this Q-function to train an agent with the position matrix abstraction. The simple agent does not take considerably fewer samples to converge, and the total time needed to first train the simple agent and then transfer exceeds the time needed to train the complex agent from scratch.