The field of continual learning and reinforcement learning is moving towards addressing the challenges of adapting to changing tasks and environments. Researchers are developing innovative approaches to mitigate catastrophic forgetting, improve task adaptation, and enhance scalability. One notable direction is the use of nonparametric statistical methods for path generation and evaluation in video games, which provides precise control and interpretable insights. Another area of focus is the development of benchmarks and evaluation protocols to assess the performance of algorithms in continual reinforcement learning. Theoretical analyses of techniques such as sample replay are also being conducted to understand their impact on forgetting. Furthermore, hybrid meta-reinforcement learning methods are being proposed to improve sample efficiency in settings with missing reward signals. Noteworthy papers in this area include:

• A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks, which introduces a benchmark for continual reinforcement learning in video game navigation scenarios.
• Replay Can Provably Increase Forgetting, which provides a theoretical analysis of sample replay in continual learning and shows that it can be harmful in certain settings.
• Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning, which proposes a novel hybrid meta-RL method that combines parameterized policy gradient-based and task inference-based few-shot meta-RL.