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Found 6 papers, 2 papers with code
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions
The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework.
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping
Developing agents that can execute multiple skills by learning from pre-collected datasets is an important problem in robotics, where online interaction with the environment is extremely time-consuming.
Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision
Finally, we train a goal-conditioned policy network with goals sampled from the goal memory and reward it by the reachability network and the goal memory.
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
In this work, we present a memory-augmented approach for image-goal navigation.
Learning to Visually Navigate in Photorealistic Environments Without any Supervision
Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training.
Understanding Image Quality and Trust in Peer-to-Peer Marketplaces
In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces.
