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Found 4 papers, 4 papers with code
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning Systems
For multi-agent reinforcement learning systems (MARLS), the problem formulation generally involves investing massive reward engineering effort specific to a given problem.
RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification
Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim.
Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
The behavior policy is learnt by agents trained with the help from deep reinforcement learning models.
Systematically designing better instance counting models on cell images with Neural Arithmetic Logic Units
These numerically biased units are added in the form of residual concatenated layers to original architectures and a comparative experimental study is done with these newly proposed changes.
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