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Found 5 papers, 4 papers with code
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions.
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
In this work, we propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs to provide a graphical representation of the sub-goals to a reinforcement learning (RL) agent that does not have access to the transition dynamics of the environment.
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
We use Generative Adversarial Networks (GANs) along with a cycle-consistency loss to map the observations between the source and target domains and later use this learned mapping to clone the successful source task behavior policy to the target domain.
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e. g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e. g, Q-Learning for Reward Machines).
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent's environment (i. e., novelties).
