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Found 8 papers, 5 papers with code
A Benchmark and a Baseline for Robust Multi-view Depth Estimation
We show that recent approaches do not generalize across datasets in this setting.
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization
Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots.
Scaling Imitation Learning in Minecraft
Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments.
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards.
CrossNorm: On Normalization for Off-Policy Reinforcement Learning
Off-policy temporal difference (TD) methods are a powerful class of reinforcement learning (RL) algorithms.
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
Sample efficiency is a crucial problem in deep reinforcement learning.
Motion Perception in Reinforcement Learning with Dynamic Objects
In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken.
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators.
