add BlendRL paper

references.bib

@@ -1,3 +1,16 @@
+@misc{shindo2024blendrl_arxiv,
+  Anote={./images/shindo2024blendrl.png},
+  author = {Hikaru Shindo and Quentin Delfosse and Devendra Singh Dhami and Kristian Kersting},
+  title = {BlendRL: A Framework for Merging Symbolic and Neural Policy Learning},
+  Keywords = {Reinforcement Learning,  Neuro-Symbolic AI, Differentiable Reasoning, Interpretable and Explainable AI},
+  Howpublished = {arXiv preprint arXiv:2410.11689},
+  year = {2024},
+  Url = {https://arxiv.org/abs/2410.11689},
+  Pages = {},
+  Crossref={https://github.com/ml-research/blendrl},
+  Note = {Humans can leverage both abstract reasoning and intuitive reactions. In contrast, reinforcement learning policies are typically encoded in either opaque systems like neural networks or symbolic systems that rely on predefined symbols and rules. This disjointed approach severely limits the agents’ capabilities, as they often lack either the flexible low-level reaction characteristic of neural agents or the interpretable reasoning of symbolic agents. To overcome this challenge, we introduce BlendRL, a neuro-symbolic RL framework that harmoniously integrates both paradigms within RL agents that use mixtures of both logic and neural policies. We empirically demonstrate that BlendRL agents outperform both neural and symbolic baselines in standard Atari environments, and showcase their robustness to environmental changes. Additionally, we analyze the interaction between neural and symbolic policies, illustrating how their hybrid use helps agents overcome each other's limitations.}
+}
+
 @inproceedings{hintersdorf2024balancingtransparency,
   anote = {./images/hintersdorf2024balancingtransparency.png},
   author = {Dominik Hintersdorf and Lukas Struppek and Kristian Kersting},