Search Results for author:
Found 31 papers, 10 papers with code
A multi-agent control framework for co-adaptation in brain-computer interfaces
In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response.
Taming the Noise in Reinforcement Learning via Soft Updates
We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process.
Optimal Selective Attention in Reactive Agents
One attempt to deal with this is to focus on reactive policies, that only base their actions on the most recent observation.
Principled Option Learning in Markov Decision Processes
It is well known that options can make planning more efficient, among their many benefits.
Information-Theoretic Methods for Planning and Learning in Partially Observable Markov Decision Processes
Bounded agents are limited by intrinsic constraints on their ability to process information that is available in their sensors and memory and choose actions and memory updates.
Multi-Level Discovery of Deep Options
Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces.
DART: Noise Injection for Robust Imitation Learning
One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet.
Robotics
RLlib: Abstractions for Distributed Reinforcement Learning
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.
Parametrized Hierarchical Procedures for Neural Programming
Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.
Hierarchical Variational Imitation Learning of Control Programs
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
We also introduce an open-source environment for Barrage Stratego, a variant of Stratego with an approximate game tree complexity of $10^{50}$.
A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
We use Q* search to solve the Rubik's cube when formulated with a large action space that includes 1872 meta-actions and find that this 157-fold increase in the size of the action space incurs less than a 4-fold increase in computation time and less than a 3-fold increase in number of nodes generated when performing Q* search.
XDO: A Double Oracle Algorithm for Extensive-Form Games
NXDO is the first deep RL method that can find an approximate Nash equilibrium in high-dimensional continuous-action sequential games.
Improving Social Welfare While Preserving Autonomy via a Pareto Mediator
Machine learning algorithms often make decisions on behalf of agents with varied and sometimes conflicting interests.
Modular Framework for Visuomotor Language Grounding
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research.
Independent Natural Policy Gradient Always Converges in Markov Potential Games
Recent results have shown that independent policy gradient converges in MPGs but it was not known whether Independent Natural Policy Gradient converges in MPGs as well.
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Under the belief that $\beta$ is closely related to the (state dependent) model uncertainty, Entropy Regularized Q-Learning (EQL) further introduces a principled scheduling of $\beta$ by maintaining a collection of the model parameters that characterizes model uncertainty.
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning
Maximum Entropy Reinforcement Learning (MaxEnt RL) algorithms such as Soft Q-Learning (SQL) and Soft Actor-Critic trade off reward and policy entropy, which has the potential to improve training stability and robustness.
Target Entropy Annealing for Discrete Soft Actor-Critic
Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings.
Anytime PSRO for Two-Player Zero-Sum Games
PSRO is based on the tabular double oracle (DO) method, an algorithm that is guaranteed to converge to a Nash equilibrium, but may increase exploitability from one iteration to the next.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning to Query Internet Text for Informing Reinforcement Learning Agents
We show that our method correctly learns to execute queries to maximize reward in a reinforcement learning setting.
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games
Instead of adding only deterministic best responses to the opponent's least exploitable population mixture, SP-PSRO also learns an approximately optimal stochastic policy and adds it to the population as well.
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
In this paper, we propose Feasible Adversarial Robust RL (FARR), a novel problem formulation and objective for automatically determining the set of environment parameter values over which to be robust.
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
On a set of 26 benchmark Atari environments, MeanQ outperforms all tested baselines, including the best available baseline, SUNRISE, at 100K interaction steps in 16/26 environments, and by 68% on average.
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world.
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities.
Learning to Design Analog Circuits to Meet Threshold Specifications
In this work, we propose a method for generating from simulation data a dataset on which a system can be trained via supervised learning to design circuits to meet threshold specifications.
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
We evaluate our method in the classic videogame NetHack and the text environment ScienceWorld to demonstrate SSO's ability to optimize a set of skills and perform in-context policy improvement.
Moonwalk: Inverse-Forward Differentiation
Our method, Moonwalk, has a time complexity linear in the depth of the network, unlike the quadratic time complexity of na\"ive forward, and empirically reduces computation time by several orders of magnitude without allocating more memory.
Reinforcement Learning from Delayed Observations via World Models
In standard Reinforcement Learning settings, agents typically assume immediate feedback about the effects of their actions after taking them.
