Search Results for author:
Found 4 papers, 2 papers with code
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
To this end, we introduce NovelGym, a flexible and adaptable ecosystem designed to simulate gridworld environments, serving as a robust platform for benchmarking reinforcement learning (RL) and hybrid planning and learning agents in open-world contexts.
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.
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).
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains.
