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Found 4 papers, 1 papers with code
URLB: Unsupervised Reinforcement Learning Benchmark
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks.
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers
In this paper, we investigate how we can leverage large reward-free (i. e. task-agnostic) offline datasets of prior interactions to pre-train agents that can then be fine-tuned using a small reward-annotated dataset.
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
When combined together, they substantially improve the performance and generalization of offline RL policies.
Data-Efficient Exploration with Self Play for Atari
To alleviate the reliance on reward engineering it is important to develop RL algorithms capable of efficiently acquiring skills with no rewards extrinsic to the agent.
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