Search Results for author:
Found 15 papers, 3 papers with code
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games.
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning
Our approach facilitates the learning of new policies by (1) maximizing the target MDP reward with the help of the black-box option, and (2) returning the agent to states in the learned initiation set of the black-box option where it is already optimal.
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning
We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills.
Hyperoctahedral Homology for Involutive Algebras
Hyperoctahedral homology is the homology theory associated to the hyperoctahedral crossed simplicial group.
Algebraic Topology 55N35, 13D03, 55U15, 55P47
Affordance as general value function: A computational model
General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
The value function lies in the heart of Reinforcement Learning (RL), which defines the long-term evaluation of a policy in a given state.
Learning predictive representations in autonomous driving to improve deep reinforcement learning
Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot.
Perception as prediction using general value functions in autonomous driving applications
We demonstrate perception as prediction by learning to predict an agent's front safety and rear safety with GVFs, which encapsulate anticipation of the behavior of the vehicle in front and in the rear, respectively.
Efficient decorrelation of features using Gramian in Reinforcement Learning
Learning good representations is a long standing problem in reinforcement learning (RL).
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks.
Robotics
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning
We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a $\textit{fixed}$ number of future time steps.
Importance Resampling for Off-policy Prediction
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning.
Importance Resampling for Off-policy Policy Evaluation
We propose Importance Resampling (IR) for off-policy learning, that resamples experience from the replay buffer and applies a standard on-policy update.
