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Found 6 papers, 1 papers with code
Unsupervised Learning of Effective Actions in Robotics
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics.
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains.
How do Offline Measures for Exploration in Reinforcement Learning behave?
Sufficient exploration is paramount for the success of a reinforcement learning agent.
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum.
Model-based Reinforcement Learning
reinforcement-learning
+1
How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning
We present a novel arbitration mechanism between learning systems that explicitly measures performance and cost.
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search
Most Deep Reinforcement Learning methods perform local search and therefore are prone to get stuck on non-optimal solutions.
Model-based Reinforcement Learning
reinforcement-learning
+1
