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Found 20 papers, 7 papers with code
Generalized Mean Estimation in Monte-Carlo Tree Search
Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w. r. t.
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning
Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function.
MushroomRL: Simplifying Reinforcement Learning Research
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments.
Deep Reinforcement Learning with Weighted Q-Learning
In this regard, Weighted Q-Learning (WQL) effectively reduces bias and shows remarkable results in stochastic environments.
Self-Paced Deep Reinforcement Learning
Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning.
Convex Regularization in Monte-Carlo Tree Search
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making.
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives.
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance.
Model-based Reinforcement Learning
Model Predictive Control
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Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Reactive motion generation problems are usually solved by computing actions as a sum of policies.
Boosted Curriculum Reinforcement Learning
This approach, which we refer to as boosted curriculum reinforcement learning (BCRL), has the benefit of naturally increasing the representativeness of the functional space by adding a new residual each time a new task is presented.
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search
In this work, we propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization.
Monte-Carlo tree search with uncertainty propagation via optimal transport
We introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node.
On the Benefit of Optimal Transport for Curriculum Reinforcement Learning
In this work, we focus on framing curricula as interpolations between task distributions, which has previously been shown to be a viable approach to CRL.
Domain Randomization via Entropy Maximization
Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL).
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i. e., rational strategy, corresponds to a Nash equilibrium.
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems.
Contact Energy Based Hindsight Experience Prioritization
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences.
Parameterized Projected Bellman Operator
We formulate an optimization problem to learn PBO for generic sequential decision-making problems, and we theoretically analyze its properties in two representative classes of RL problems.
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning.
Iterated $Q$-Network: Beyond the One-Step Bellman Operator
Value-based Reinforcement Learning (RL) methods rely on the application of the Bellman operator, which needs to be approximated from samples.
