Search Results for author:
Found 11 papers, 2 papers with code
Estimating Q(s,s') with Deterministic Dynamics Gradients
In this paper, we introduce a novel form of a value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
Hard Attention Control By Mutual Information Maximization
We also show that the agent's internal representation of the surroundings, a live mental map, can be used for control in two partially observable reinforcement learning tasks.
A Bayesian Framework for Nash Equilibrium Inference in Human-Robot Parallel Play
We showed that using a Bayesian approach to infer the equilibrium enables the robot to complete the task with less than half the number of collisions while also reducing the task execution time as compared to the best baseline.
Supportive Actions for Manipulation in Human-Robot Coworker Teams
We compare two robot modes in a shared table pick-and-place task: (1) Task-oriented: the robot only takes actions to further its own task objective and (2) Supportive: the robot sometimes prefers supportive actions to task-oriented ones when they reduce future goal-conflicts.
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning
This work considers two distinct settings: imitation learning and goal-conditioned reinforcement learning.
Learning to Compose Skills
We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills.
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, simpler domains to more complex ones using a recurrent attention mechanism.
Environment-Independent Task Specifications via GLTL
We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent.
Perceptual Reward Functions
Reinforcement learning problems are often described through rewards that indicate if an agent has completed some task.
