Search Results for author:
Found 19 papers, 8 papers with code
Learning the Target Network in Function Space
We focus on the task of learning the value function in the reinforcement learning (RL) setting.
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e. g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data.
Characterizing the Action-Generalization Gap in Deep Q-Learning
We study the action generalization ability of deep Q-learning in discrete action spaces.
Faster Deep Reinforcement Learning with Slower Online Network
In this paper we endow two popular deep reinforcement learning algorithms, namely DQN and Rainbow, with updates that incentivize the online network to remain in the proximity of the target network.
Coarse-Grained Smoothness for RL in Metric Spaces
We propose a new coarse-grained smoothness definition that generalizes the notion of Lipschitz continuity, is more widely applicable, and allows us to compute significantly tighter bounds on Q-functions, leading to improved learning.
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
We propose a variant of COACH, episodic COACH (E-COACH), which we prove converges for all three types.
Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration.
Learning State Abstractions for Transfer in Continuous Control
In this work, we answer this question in the affirmative, where we take "simple learning algorithm" to be tabular Q-Learning, the "good representations" to be a learned state abstraction, and "challenging problems" to be continuous control tasks.
Deep Radial-Basis Value Functions for Continuous Control
We show that the maximum action-value with respect to a deep RBVF can be approximated easily and accurately.
Lipschitz Lifelong Reinforcement Learning
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks.
Combating the Compounding-Error Problem with a Multi-step Model
In this paper, we address the compounding-error problem by introducing a multi-step model that directly outputs the outcome of executing a sequence of actions.
Model-based Reinforcement Learning
reinforcement-learning
+2
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model.
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes.
Model-based Reinforcement Learning
reinforcement-learning
+2
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
Learning a generative model is a key component of model-based reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
Lipschitz Continuity in Model-based Reinforcement Learning
We go on to prove an error bound for the value-function estimate arising from Lipschitz models and show that the estimated value function is itself Lipschitz.
Model-based Reinforcement Learning
reinforcement-learning
+2
Mean Actor Critic
We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning.
Ranked #1 on
Continuous Control
on Cart Pole (OpenAI Gym)
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors.
Sample-efficient Deep Reinforcement Learning for Dialog Control
Representing a dialog policy as a recurrent neural network (RNN) is attractive because it handles partial observability, infers a latent representation of state, and can be optimized with supervised learning (SL) or reinforcement learning (RL).
An Alternative Softmax Operator for Reinforcement Learning
A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average.
