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Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
However, the relationship between specific exploration characteristics and effective transfer learning in deep RL has not been characterized.
Novelty Detection in Reinforcement Learning with World Models
We refer to the sudden change in visual properties or state transitions as novelties.
Neuro-Symbolic World Models for Adapting to Open World Novelty
Additionally, WorldCloner augments the policy learning process using imagination-based adaptation, where the world model simulates transitions of the post-novelty environment to help the policy adapt.
The Role of Exploration for Task Transfer in Reinforcement Learning
The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the context of learning the optimal policy for a single learning task.
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
We provide an ontology of for novelties most relevant to sequential decision making, which distinguishes between novelties that affect objects versus actions, unary properties versus non-unary relations, and the distribution of solutions to a task.
