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Found 11 papers, 7 papers with code
Mastering Atari Games with Limited Data
Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal.
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Atari Games 100k
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Discrete Predictive Representation for Long-horizon Planning
Discrete representations have been key in enabling robots to plan at more abstract levels and solve temporally-extended tasks more efficiently for decades.
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance.
Sparse Graphical Memory for Robust Planning
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons.
Hallucinative Topological Memory for Zero-Shot Visual Planning
In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e. g., images obtained from self-supervised robot interaction.
Deep Variational Semi-Supervised Novelty Detection
In this work, we propose two variational methods for training VAEs for SSAD.
Learning to Manipulate Deformable Objects without Demonstrations
Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points.
Learning Robotic Manipulation through Visual Planning and Acting
We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.
Learning Plannable Representations with Causal InfoGAN
Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations.
Model-Ensemble Trust-Region Policy Optimization
In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world.
