Offline Reinforcement Learning (Offline RL) involves learning policies from a static dataset without further interactions with the environment, making it suitable for high-stakes scenarios where data collection is costly or risky. This paper investigates the generalization capabilities of Implicit Q-Learning (IQL), an offline RL algorithm, compared to Behavioral Cloning (BC). We adapt the IQL algorithm for discrete control and evaluate both IQL and BC in a four-room environment using training datasets generated from different behavioral policies. Performance is assessed based on average rewards over various test seeds, on reachable and unreachable tasks, as well as the training set. Our results indicate that BC consistently outperforms IQL across all scenarios, although IQL reaches peak performance faster. This study highlights the need for further research into offline RL algorithms for better generalization and more robust performance in diverse environments. Full code available on GitHub.

This paper examines the generalization capabilities of the Soft Actor-Critic (SAC) algorithm when combined with Behavioral Cloning (BC) in a MiniGrid Four-Room Environment. Reinforcement learning (RL), particularly offline, is important for tasks where interactions with the environments are risky or costly, and this research focuses on multi-task environments where generalizability to new tasks is crucial. Our findings indicate that SAC+BC can achieve generalization performance close to BC. Notably, while BC shows robustness across various dataset characteristics (quality, diversity, size), SAC alone struggles without integrating BC, highlighting the enhancement in generalization brought by this hybrid approach. Furthermore, an increased data size only enhances generalizability when introducing greater diversity. However, these results are constrained by hardware limitations, suggesting that further hyperparameter optimization and using more seeds could validate and possibly enhance our findings, demonstrating that SAC+BC is even more effective
than shown. The implementation details and the source code for this study are available on GitHub at https://github.com/AxelGeist/multi-task-offlinereinforcement-learning.

In the field of Artificial Intelligence (AI), techniques like Reinforcement Learning (RL) and Decision Transformer (DT) are utilized by machines to learn from experiences and solve problems. The distinction between offline and online learning determines whether the machine learns from a live environment or simply observes pre-recorded actions. The difference between single-task and multi-task settings indicates whether the machine can handle similar but not identical tasks. Multi-task, offline learning, the focus of this paper, allows machines to address a variety of related tasks, based on a pre-recorded set of experiences. This approach is particularly valuable in situations where traditional training methods are costly or challenging. For instance, in robotics, multi-task, offline learning enables robots to use experiences from various tasks, such as picking up objects, to solve new problems like placing them down. This research paper explores the effectiveness of Decision Transformers in multi-task environments through theoretical discussions and practical examples. It also tries to answer the question, of how introducing sub-optimal training data, affects the performance or generalisation ability of the model.

Recent work has shown that offline reinforcement learning (RL) does not generalize well to new environments compared to behavioral cloning (BC). We propose WSAC-N, an ensemble model of soft actor-critics with weights to de-emphasize actions with high variance. We compare the zero-shot generalization abilities of WSAC-N with the baseline BC in a four-room maze-like environment, testing on unseen tasks. Our findings indicate that WSAC-N has worse zero-shot generalization compared to BC, aligning with previous work. Additionally, we investigate the impact of dataset characteristics on generalization, finding that dataset size has a negligible impact, while the quality of trajectories generally has a positive effect. These results are consistent with prior research.

Reinforcement learning (RL) is a type of machine learning where a model learns by
making an observation of the current state it is in, picking out an action to execute, and
observing the reward of said action, after which it receives the next state and repeats the
cycle until it reaches its goal. The traditional online training approach allows the agent to
directly interact with the live environment, but that is not always possible due to the live
environment possibly being too dangerous or costly to train in. In cases like these offline
training, which instead trains the agent on already pre-collected datasets of previously
mentioned interactions and tries to learn a better policy than the one used for collection,
offers a viable alternative by employing Q-Learning methods like CQL. However, prior
studies, such as Mediratta et al., have suggested that Behavior Cloning (BC), a
type of imitation cloning, may outperform modern offline RL methods in the multi-task
setting, where model generalization is tested on new or similar tasks rather than the ones
trained on. Considering these results, it begs the question whether it is worthwhile to
employ modern Q-Learning methods designed to derive a better policy than the one used
to collect the data, especially when they are unable to outperform standard imitation
learning.
This study seeks to reproduce and extend these findings within a custom environment.
The results reveal that, contrary to the aforementioned report, BC does not consistently
outperform CQL. Both machine learning methods exhibit comparable performance across
datasets varying in diversity and size. Additionally, incorporating more diverse data
significantly enhances generalization performance.

Autonomous driving is a rapidly evolving field that aims to enhance road safety and reduce accidents through the use of advanced software and hardware technologies. Reinforcement learning (RL) combined with deep neural networks has emerged as a promising approach for training autonomous agents. This research paper investigates three exploration algorithms —Epsilon-Greedy, Random Network Distillation (RND), and Bootstrapped Deep Q-Network (DQN)— within the context of autonomous driving. Performance is assessed based on episodic returns in training and testing environments, as well as the time required to train the networks. The results show significant improvement in learning capability using Bootstrapped DQN without critical differences in training time. There also exists a potential to increase episodic returns further given an increase in the number of steps to train the models.

This research paper aims to investigate the effect of entropy while training the agent on the robustness of the agent. This is important because robustness is defined as the agent's adaptability to different environments. A self-driving car should adapt to every environment that it is being used in since a mistake could cost someone's life. Therefore, robustness is of great importance in self-driving cars. An increase in entropy would promote the exploration of different strategies and prevent convergence on local maximum results. In order to test entropy values in training, the Soft-Actor Critic algorithm is used. The algorithm is run on a simulated city environment called Carla. In the end, collected data shows that the agent which is trained with a higher entropy value adapts to environments it did not train on better than the low entropy agent. However, a low entropy agent performs better in the environment it is trained in. Therefore, increasing the entropy increases robustness but it lowers the performance in the training environment itself.

Deep reinforcement learning has been a topic of research in recent years and has been expanding into the domain of autonomous driving. As autonomous driving is likely to involve people, such as daily commuters, it is necessary to ensure the machine will perform well enough in real-life environments not to put anyone at risk. There exist possible approaches to make the transition from a simulation to real life easier, such as domain randomization. This paper uses OpenAI's CarRacing-v2 environment and the CARLA simulator to investigate the effect of domain randomization on training efficiency and robustness for a Deep Q-Network algorithm for autonomous driving. The results show a decrease in training efficiency and higher variance during training for both environments. CARLA also indicates an overestimation during training. As for robustness testing, while visual domain randomization in CarRacing-v2 does not suggest a significant influence on robustness, the dynamic domain randomization in CARLA offers a positive influence toward robustness at the expense of some reward.

Reinforcement Learning (RL) has gained atten-tion as a way of creating autonomous agents for self-driving cars. This paper explores the adap- tation of the Deep Q Network (DQN), a popular deep RL algorithm, in the Carla traffic simulator for autonomous driving. It investigates the influ- ence of action space discretization and DQN ex-
tensions on training performance and robustness. Results show that action space discretization en- hances behaviour consistency but negatively af- fects Q-values, training performance, and robust- ness. Double Q-Learning decreases training per- formance and leads to suboptimal convergence, re- ducing robustness. Prioritized Experience Replay
also performs worse during training, but consis-tently outperforms in robustness testing, reward es-timation and generalization.

Autonomous driving is a complex problem that can potentially be solved using artificial intelligence. The complexity stems from the system's need to understand the surroundings and make appropriate decisions. However, there are various challenges in constructing such a sophisticated system. One of the main challenges is to make the agent learn from the environmental input and since the environment is not fully observable and constantly changing, the agent should be flexible enough to extract important information from the input. To create such an agent this paper focused on the 3 different methods, specifically the application of frame stacking, long short-term memory (LSTM), and a combination of both methods. To analyze these methods 3 Deep Q Network(DQN) based agents are created. These 3 agents have frame stacking, LSTM, and both combined methods to solve the partially observable problem of autonomous driving. Their training performances are analyzed, and the results revealed significantly different trends in the training and evaluation phase.
Especially the experiment resulted in LSTM being a more robust method but had lower performance than DQN with frame stack, which showed a trade-off between these 2 qualities of an agent. The agent with LSTM and frame stack was able to learn faster at the beginning, but as it was unstable, it got a lower return value from the training run at the end. This instability can be a problem in the real world, especially in autonomous driving, where it is very important to have robust implementation.