100 papers with code • 0 benchmarks • 2 datasets
Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.
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We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation.
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals.
In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user.
We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.
For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems.
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards.
In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.