Search Results for author:
Found 32 papers, 7 papers with code
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time.
Learning to Optimize for Reinforcement Learning
Reinforcement learning (RL) is essentially different from supervised learning and in practice these learned optimizers do not work well even in simple RL tasks.
Visual Imitation Learning with Patch Rewards
Specifically, we present Adversarial Imitation Learning with Patch Rewards (PatchAIL), which employs a patch-based discriminator to measure the expertise of different local parts from given images and provide patch rewards.
Reinforcement Learning from Diverse Human Preferences
The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques.
Imitation Learning As State Matching via Differentiable Physics
With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks.
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning
Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in the evaluation scenarios where other agents behave differently.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Boosting Offline Reinforcement Learning via Data Rebalancing
Therefore, we propose a simple yet effective method to boost offline RL algorithms based on the observation that resampling a dataset keeps the distribution support unchanged.
Efficient Offline Policy Optimization with a Learned Model
Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout.
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL.
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
EnvPool is open-sourced at https://github. com/sail-sg/envpool.
Imitation Learning via Differentiable Physics
With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks.
ETA Prediction with Graph Neural Networks in Google Maps
Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.
Emphatic Algorithms for Deep Reinforcement Learning
In this paper, we extend the use of emphatic methods to deep reinforcement learning agents.
Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.
Balancing Constraints and Rewards with Meta-Gradient D4PG
Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints.
Discovering Reinforcement Learning Algorithms
Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments.
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
In this work, we propose an algorithm based on meta-gradient descent that discovers its own objective, flexibly parameterised by a deep neural network, solely from interactive experience with its environment.
A Self-Tuning Actor-Critic Algorithm
Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters, typically requiring significant manual effort to identify hyperparameters that perform well on a new domain.
What Can Learned Intrinsic Rewards Capture?
Furthermore, we show that unlike policy transfer methods that capture "how" the agent should behave, the learned reward functions can generalise to other kinds of agents and to changes in the dynamics of the environment by capturing "what" the agent should strive to do.
Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this departs from the focus in much of machine learning on agents learning answers to externally defined questions.
Meta-Gradient Reinforcement Learning
Instead, the majority of reinforcement learning algorithms estimate and/or optimise a proxy for the value function.
Natural Value Approximators: Learning when to Trust Past Estimates
Neural networks have a smooth initial inductive bias, such that small changes in input do not lead to large changes in output.
An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning
We propose an end-to-end approach to the natural language object retrieval task, which localizes an object within an image according to a natural language description, i. e., referring expression.
Few-Shot Object Recognition from Machine-Labeled Web Images
Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources to perform object recognition on novel categories.
Bidirectional Multirate Reconstruction for Temporal Modeling in Videos
This learning process makes the learned model more capable of dealing with motion speed variance.
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.
Uncovering Temporal Context for Video Question and Answering
In this work, we introduce Video Question Answering in temporal domain to infer the past, describe the present and predict the future.
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning
In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos.
A Discriminative CNN Video Representation for Event Detection
In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available.
Event Detection using Multi-Level Relevance Labels and Multiple Features
We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available.
Complex Event Detection via Multi-source Video Attributes
Compared to complex event videos, these external videos contain simple contents such as objects, scenes and actions which are the basic elements of complex events.
