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Found 11 papers, 5 papers with code
Deep Probabilistic Movement Primitives with a Bayesian Aggregator
Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations.
Dynamic Decision Frequency with Continuous Options
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals.
A Temporal-Difference Approach to Policy Gradient Estimation
The policy gradient theorem (Sutton et al., 2000) prescribes the usage of a cumulative discounted state distribution under the target policy to approximate the gradient.
An Alternate Policy Gradient Estimator for Softmax Policies
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions.
Model-free Policy Learning with Reward Gradients
As a key component in reinforcement learning, the reward function is usually devised carefully to guide the agent.
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient
Off-policy Reinforcement Learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment.
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
Parameterized movement primitives have been extensively used for imitation learning of robotic tasks.
Dimensionality Reduction of Movement Primitives in Parameter Space
The empirical analysis shows that the dimensionality reduction in parameter space is more effective than in configuration space, as it enables the representation of the movements with a significant reduction of parameters.
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity.
A Nonparametric Off-Policy Policy Gradient
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes.
Boosted Fitted Q-Iteration
This paper is about the study of B-FQI, an Approximated Value Iteration (AVI) algorithm that exploits a boosting procedure to estimate the action-value function in reinforcement learning problems.
