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Found 17 papers, 3 papers with code
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models
In traditional statistical learning, data points are usually assumed to be independently and identically distributed (i. i. d.)
Efficient Reinforcement Learning from Partial Observability
In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior performance for algorithms that conflate observations with state.
Partially Observable Reinforcement Learning
reinforcement-learning
Rethinking Decision Transformer via Hierarchical Reinforcement Learning
Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the transformer architecture in reinforcement learning (RL).
HarmonyDream: Task Harmonization Inside World Models
Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasses components for two tasks: observation modeling and reward modeling.
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Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution.
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay
In this work, we further exploit the information in the replay memory by treating it as an empirical \emph{Replay Memory MDP (RM-MDP)}.
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL).
The In-Sample Softmax for Offline Reinforcement Learning
We highlight a simple fact: it is more straightforward to approximate an in-sample \emph{softmax} using only actions in the dataset.
Latent Variable Representation for Reinforcement Learning
Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings.
Model-based Reinforcement Learning
reinforcement-learning
+1
Understanding the Effect of Stochasticity in Policy Optimization
We study the effect of stochasticity in on-policy policy optimization, and make the following four contributions.
Understanding and Leveraging Overparameterization in Recursive Value Estimation
To better understand the utility of deep models in RL we present an analysis of recursive value estimation using overparameterized linear representations that provides useful, transferable findings.
The Curse of Passive Data Collection in Batch Reinforcement Learning
In high stake applications, active experimentation may be considered too risky and thus data are often collected passively.
On the Optimality of Batch Policy Optimization Algorithms
First, we introduce a class of confidence-adjusted index algorithms that unifies optimistic and pessimistic principles in a common framework, which enables a general analysis.
Escaping the Gravitational Pull of Softmax
Both findings are based on an analysis of convergence rates using the Non-uniform \L{}ojasiewicz (N\L{}) inequalities.
On the Global Convergence Rates of Softmax Policy Gradient Methods
First, we show that with the true gradient, policy gradient with a softmax parametrization converges at a $O(1/t)$ rate, with constants depending on the problem and initialization.
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.
Model-based Reinforcement Learning
reinforcement-learning
+1
Maximum Entropy Monte-Carlo Planning
We then extend this approach to general sequential decision making by developing a general MCTS algorithm, Maximum Entropy for Tree Search (MENTS).
