Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Adversarial Policy Gradient for Alternating Markov Games
Policy gradient reinforcement learning has been applied to two-player alternate-turn zero-sum games, e.g., in AlphaGo, self-play REINFORCE was used to improve the neural net model after supervised learning. In this paper…
Policy Gradient MethodsReinforcement LearningUnderstanding Grounded Language Learning Agents
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observab…
Grounded language learningPolicy Gradient MethodsGlobal Convergence of Policy Gradient Methods for Linearized Control Problems
Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model…
continuous-controlContinuous ControlPolicy Gradient MethodsReinforcement LearningPredicting Multiple Actions for Stochastic Continuous Control
We introduce a new approach to estimate continuous actions using actor-critic algorithms for reinforcement learning problems. Policy gradient methods usually predict one continuous action estimate or parameters of a pres…
continuous-controlContinuous ControlPolicy Gradient Methodsreinforcement-learning+2Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) pro…
Deep Reinforcement LearningPolicy Gradient MethodsQ-Learningreinforcement-learning+2Bayesian Policy Gradients via Alpha Divergence Dropout Inference
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We propose an approach which instead estimate…
continuous-controlContinuous ControlMuJoCoPolicy Gradient MethodsAdaptive Batch Size for Safe Policy Gradients
Policy gradient methods are among the best Reinforcement Learning (RL) techniques to solve complex control problems. In real-world RL applications, it is common to have a good initial policy whose performance needs to be…
Policy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Divide-and-Conquer Reinforcement Learning
Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments. However, problem…
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1Run, skeleton, run: skeletal model in a physics-based simulation
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickl…
NavigatePolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Hindsight policy gradients
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may a…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Policy Optimization by Genetic Distillation
Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide effective heuristics for search and black-b…
Deep Reinforcement LearningImitation LearningMuJoCoPolicy Gradient Methods+3Action-depedent Control Variates for Policy Optimization via Stein's Identity
Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads …
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Understanding Early Word Learning in Situated Artificial Agents
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable wo…
Grounded language learningPolicy Gradient MethodsAccelerated Reinforcement Learning
Policy gradient methods are widely used in reinforcement learning algorithms to search for better policies in the parameterized policy space. They do gradient search in the policy space and are known to converge very slo…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Stochastic Variance Reduction for Policy Gradient Estimation
Recent advances in policy gradient methods and deep learning have demonstrated their applicability for complex reinforcement learning problems. However, the variance of the performance gradient estimates obtained from th…
continuous-controlContinuous ControlMuJoCoPolicy Gradient Methods+3Manifold Regularization for Kernelized LSTD
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL). It is a necessary component of policy iteration and can be used for variance reduction in policy gradient…
Policy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Cold-Start Reinforcement Learning with Softmax Policy Gradient
Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning meth…
Image CaptioningPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Policy gradient methods in reinforcement learning have become increasingly prevalent for state-of-the-art performance in continuous control tasks. Novel methods typically benchmark against a few key algorithms such as de…
continuous-controlContinuous ControlDeep Reinforcement LearningPolicy Gradient Methods+3Proximal Policy Optimization Algorithms
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochas…
Continuous ControlDota 2Multi-Goal Reinforcement LearningNeural Architecture Search+4Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines
We show how an action-dependent baseline can be used by the policy gradient theorem using function approximation, originally presented with action-independent baselines by (Sutton et al. 2000).
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)