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Found 5 papers, 1 papers with code
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts
While more sophisticated exploration strategies can excel in specific, often sparse reward environments, existing simpler approaches, such as $\epsilon$-greedy, persist in outperforming them across a broader spectrum of domains.
Posterior Sampling for Deep Reinforcement Learning
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies.
Computational Efficiency
Model-based Reinforcement Learning
+2
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task.
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks.
Model-based Reinforcement Learning
reinforcement-learning
+2
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.
