Search Results for author:
Found 12 papers, 6 papers with code
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
Along with this model, we also provide a set of reinforcement learning tasks, including reference motion tracking, running, and neck control, used to infer muscle actuation patterns.
Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution
This paper proposes a novel method for diversity-based policy search via Neuroevolution, that leverages learned representations of the policy network parameters, by performing policy search in this learned representation space.
Policy Manifold Search for Improving Diversity-based Neuroevolution
Our approach iteratively collects policies according to the QD framework, in order to (i) build a collection of diverse policies, (ii) use it to learn a latent representation of the policy parameters, (iii) perform policy search in the learned latent space.
Learning to Represent Action Values as a Hypergraph on the Action Vertices
To test this, we set forth the action hypergraph networks framework -- a class of functions for learning action representations in multi-dimensional discrete action spaces with a structural inductive bias.
Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data
This network is trained to be able to detect a casualty using a point-cloud data input.
Active learning via informed search in movement parameter space for efficient robot task learning and transfer
We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function.
Exploring Restart Distributions
We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution.
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Being able to reach any desired location in the environment can be a valuable asset for an agent.
Goal-oriented Trajectories for Efficient Exploration
Exploration is a difficult challenge in reinforcement learning and even recent state-of-the art curiosity-based methods rely on the simple epsilon-greedy strategy to generate novelty.
Time Limits in Reinforcement Learning
In case (ii), the time limits are not part of the environment and are only used to facilitate learning.
Action Branching Architectures for Deep Reinforcement Learning
This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension.
Visuospatial Skill Learning for Robots
A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks.
