Search Results for author:
Found 7 papers, 3 papers with code
Hierarchical Imitation Learning for Stochastic Environments
However, such methods are often inappropriate for stochastic environments where the agent must also react to external factors: because agent types are inferred from the observed future trajectory during training, these environments require that the contributions of internal and external factors to the agent behaviour are disentangled and only internal factors, i. e., those under the agent's control, are encoded in the type.
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator.
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.
Learning from Demonstration in the Wild
Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical.
Fast Context Adaptation via Meta-Learning
We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable.
CAML: Fast Context Adaptation via Meta-Learning
We propose CAML, a meta-learning method for fast adaptation that partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks.
TACO: Learning Task Decomposition via Temporal Alignment for Control
Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks.
