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Found 9 papers, 4 papers with code
Effective Reward Specification in Deep Reinforcement Learning
In the last decade, Deep Reinforcement Learning has evolved into a powerful tool for complex sequential decision-making problems.
Efficient Biological Data Acquisition through Inference Set Design
In drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs.
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
To address this, we introduce various strategies for learning the GFlowNet backward policy and thus demonstrate how additional constraints can be integrated into the GFlowNet MDP framework.
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
In recent years, in-silico molecular design has received much attention from the machine learning community.
Direct Behavior Specification via Constrained Reinforcement Learning
The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator.
Option-Critic in Cooperative Multi-agent Systems
In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, 1999).
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Finally, we analyze the effects of our proposed methods on the policies that our agents learn and show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.
Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data
We find that using such constraints allow to stabilize the training of recurrent adversarial architectures for animation generation.
