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Found 15 papers, 8 papers with code
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment.
Understanding What Affects Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence
Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL).
Exploration and Anti-Exploration with Distributional Random Network Distillation
To address this issue, we introduce the Distributional RND (DRND), a derivative of the RND.
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
The direct preference optimization (DPO) method, effective in fine-tuning large language models, eliminates the necessity for a reward model.
The primacy bias in Model-based RL
The primacy bias in deep reinforcement learning (DRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of DRL algorithms.
Zero-shot Preference Learning for Offline RL via Optimal Transport
In this paper, we propose a novel zero-shot preference-based RL algorithm that leverages labeled preference data from source tasks to infer labels for target tasks, eliminating the requirement for human queries.
Normalization Enhances Generalization in Visual Reinforcement Learning
Though normalization techniques have demonstrated huge success in supervised and unsupervised learning, their applications in visual RL are still scarce.
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse
Empirical results show that SMR significantly boosts the sample efficiency of the base methods across most of the evaluated tasks without any hyperparameter tuning or additional tricks.
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO.
State Advantage Weighting for Offline RL
We present state advantage weighting for offline reinforcement learning (RL).
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination
Model-based RL methods offer a richer dataset and benefit generalization by generating imaginary trajectories with either trained forward or reverse dynamics model.
Mildly Conservative Q-Learning for Offline Reinforcement Learning
The distribution shift between the learned policy and the behavior policy makes it necessary for the value function to stay conservative such that out-of-distribution (OOD) actions will not be severely overestimated.
Value Activation for Bias Alleviation: Generalized-activated Deep Double Deterministic Policy Gradients
It is vital to accurately estimate the value function in Deep Reinforcement Learning (DRL) such that the agent could execute proper actions instead of suboptimal ones.
Efficient Continuous Control with Double Actors and Regularized Critics
First, we uncover and demonstrate the bias alleviation property of double actors by building double actors upon single critic and double critics to handle overestimation bias in DDPG and underestimation bias in TD3 respectively.
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency.
