Search Results for author:
Found 40 papers, 15 papers with code
A Game Theoretic Perspective on Model-Based Reinforcement Learning
We point out that a large class of MBRL algorithms can be viewed as a game between two players: (1) a policy player, which attempts to maximize rewards under the learned model; (2) a model player, which attempts to fit the real-world data collected by the policy player.
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks.
Train Offline, Test Online: A Real Robot Learning Benchmark
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data.
Masked Trajectory Models for Prediction, Representation, and Control
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making.
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Contrary to inferences from prior work, we find that scaling dataset size and diversity does not improve performance universally (but does so on average).
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks.
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework.
Model-based Reinforcement Learning
reinforcement-learning
+1
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
On a real robot setup, CACTI enables efficient training of a single policy that can perform 10 manipulation tasks involving kitchen objects, and is robust to varying layouts of distractors.
Real World Offline Reinforcement Learning with Realistic Data Source
Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience.
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Task specification is at the core of programming autonomous robots.
R3M: A Universal Visual Representation for Robot Manipulation
We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks.
Policy Architectures for Compositional Generalization in Control
Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment.
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
In this context, we revisit and study the role of pre-trained visual representations for control, and in particular representations trained on large-scale computer vision datasets.
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors.
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers
In this paper, we investigate how we can leverage large reward-free (i. e. task-agnostic) offline datasets of prior interactions to pre-train agents that can then be fine-tuned using a small reward-annotated dataset.
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.
Visual Adversarial Imitation Learning using Variational Models
We consider a setting where an agent is provided a fixed dataset of visual demonstrations illustrating how to perform a task, and must learn to solve the task using the provided demonstrations and unsupervised environment interactions.
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
When combined together, they substantially improve the performance and generalization of offline RL policies.
Decision Transformer: Reinforcement Learning via Sequence Modeling
In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.
Ranked #3 on
Offline RL
on D4RL
Variational Model-Based Imitation Learning in High-Dimensional Observation Spaces
We consider the problem setting of imitation learning where the agent is provided a fixed dataset of demonstrations.
COMBO: Conservative Offline Model-Based Policy Optimization
We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model.
Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
Offline Reinforcement Learning from Images with Latent Space Models
In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces.
MOReL: Model-Based Offline Reinforcement Learning
In this work, we present MOReL, an algorithmic framework for model-based offline RL.
MOReL : Model-Based Offline Reinforcement Learning
In this work, we present MOReL, an algorithmic framework for model-based offline RL.
A Game Theoretic Framework for Model Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data.
Model-based Reinforcement Learning
reinforcement-learning
+1
Lyceum: An efficient and scalable ecosystem for robot learning
We introduce Lyceum, a high-performance computational ecosystem for robot learning.
Meta-Learning with Implicit Gradients
By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer.
Online Meta-Learning
Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch.
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning.
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators.
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Reinforcement learning has emerged as a promising methodology for training robot controllers.
Model-based Reinforcement Learning
reinforcement-learning
+1
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
Divide-and-Conquer Reinforcement Learning
In this paper, we develop a novel algorithm that instead partitions the initial state space into "slices", and optimizes an ensemble of policies, each on a different slice.
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency.
Towards Generalization and Simplicity in Continuous Control
This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of continuous control tasks, including the OpenAI gym benchmarks.
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks.
Identifying Topology of Power Distribution Networks Based on Smart Meter Data
In a power distribution network, the network topology information is essential for an efficient operation of the network.
A Novel Approach for Phase Identification in Smart Grids Using Graph Theory and Principal Component Analysis
Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer.
Network Topology Identification using PCA and its Graph Theoretic Interpretations
We show that identification is equivalent to learning a model $\mathbf{A_n}$ which captures the approximate linear relationships between the different variables comprising $\mathbf{X}$ (i. e. of the form $\mathbf{A_n X \approx 0}$) such that $\mathbf{A_n}$ is full rank (highest possible) and consistent with a network node-edge incidence structure.
