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Found 17 papers, 10 papers with code
Imitation Learning from Observation through Optimal Transport
Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance of demonstrated actions.
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states, such as physical control problems.
IL-flOw: Imitation Learning from Observation using Normalizing Flows
We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only.
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy.
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data
In this work, we analyze the effectiveness of the Bellman equation as a proxy objective for value prediction accuracy in off-policy evaluation.
A Minimalist Approach to Offline Reinforcement Learning
Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy.
Practical Marginalized Importance Sampling with the Successor Representation
We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy.
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-uniform probability proportionate to their temporal-difference error.
Benchmarking Batch Deep Reinforcement Learning Algorithms
Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment.
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Mesh models are a promising approach for encoding the structure of 3D objects.
Ranked #1 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Off-Policy Deep Reinforcement Learning without Exploration
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection.
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform.
Where Off-Policy Deep Reinforcement Learning Fails
This work examines batch reinforcement learning--the task of maximally exploiting a given batch of off-policy data, without further data collection.
Sentiment Analysis: It's Complicated!
In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30{\%} of the originally annotated data.
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
We consider the problem of scaling deep generative shape models to high-resolution.
Ranked #2 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Addressing Function Approximation Error in Actor-Critic Methods
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
Ranked #2 on
Continuous Control
on Lunar Lander (OpenAI Gym)
