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Found 19 papers, 9 papers with code
Planning to Explore via Latent Disagreement
To solve complex tasks, intelligent agents first need to explore their environments.
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
Through our experiments in five locomotion and manipulation environments, we demonstrate that METRA can discover a variety of useful behaviors even in complex, pixel-based environments, being the first unsupervised RL method that discovers diverse locomotion behaviors in pixel-based Quadruped and Humanoid.
Planning Goals for Exploration
We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize exploration.
Learning General World Models in a Handful of Reward-Free Deployments
We then present CASCADE, a novel approach for self-supervised exploration in this new setting.
Discovering and Achieving Goals via World Models
How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision?
Transferable Visual Control Policies Through Robot-Awareness
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.
Model-Based Reinforcement Learning via Latent-Space Collocation
The resulting latent collocation method (LatCo) optimizes trajectories of latent states, which improves over previously proposed shooting methods for visual model-based RL on tasks with sparse rewards and long-term goals.
Model-based Reinforcement Learning
reinforcement-learning
+1
Discovering and Achieving Goals with World Models
How can an artificial agent learn to solve a wide range of tasks in a complex visual environment in the absence of external supervision?
Reinforcement Learning with Videos: Combining Offline Observations with Interaction
In this paper, we consider the question: can we perform reinforcement learning directly on experience collected by humans?
Simple and Effective VAE Training with Calibrated Decoders
We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training.
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations.
Planning to Explore via Self-Supervised World Models
Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge.
Learning Predictive Models From Observation and Interaction
Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes.
Goal-Conditioned Video Prediction
Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.
Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction
To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.
Learning what you can do before doing anything
We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions.
Predicting the Future with Transformational States
We couple this latent state with a recurrent neural network (RNN) core that predicts future frames by transforming past states into future states by applying the accumulated state transformation with a learned operator.
