Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes $\mathtt{VRMPO}$ algorithm: a sample efficient policy gradient method with stochastic mirror descent. In $\mathtt{VR…
Continuous ControlPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1Ranking Policy Gradient
Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art estimates the optimal action values while it usually involves an extensive search over the state-action space and unstabl…
Policy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Ekar: An Explainable Method for Knowledge Aware Recommendation
This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which general…
Knowledge-Aware RecommendationKnowledge GraphsPolicy Gradient MethodsRecommendation Systems+1Entropic Risk Measure in Policy Search
With the increasing pace of automation, modern robotic systems need to act in stochastic, non-stationary, partially observable environments. A range of algorithms for finding parameterized policies that optimize for long…
Policy Gradient MethodsGlobal Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies
Policy gradient (PG) methods are a widely used reinforcement learning methodology in many applications such as video games, autonomous driving, and robotics. In spite of its empirical success, a rigorous understanding of…
Autonomous DrivingPolicy Gradient MethodsReinforcement LearningIs the Policy Gradient a Gradient?
The policy gradient theorem describes the gradient of the expected discounted return with respect to an agent's policy parameters. However, most policy gradient methods drop the discount factor from the state distributio…
Open-Ended Question AnsweringPolicy Gradient MethodsA Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression
Image compression is an essential approach for decreasing the size in bytes of the image without deteriorating the quality of it. Typically, classic algorithms are used but recently deep-learning has been successfully ap…
DecoderImage CompressionPolicy Gradient Methodsreinforcement-learning+3Global Optimality Guarantees For Policy Gradient Methods
Policy gradients methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a parameterized class of polices. Unfortunately, even for simple control problems solvable by …
Policy Gradient MethodsReinforcement LearningNeural Replicator Dynamics
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarit…
counterfactualDeep Reinforcement LearningPolicy Gradient MethodsReinforcement LearningDiversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies
Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain e…
DiversityPolicy Gradient MethodsReinforcement LearningPolicy Search by Target Distribution Learning for Continuous Control
We observe that several existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may suffer from overly large gradients when the current policy is close to deterministic (even in some very simple envi…
continuous-controlContinuous ControlMuJoCoPolicy Gradient Methods+2Distributional Policy Optimization: An Alternative Approach for Continuous Control
We identify a fundamental problem in policy gradient-based methods in continuous control. As policy gradient methods require the agent's underlying probability distribution, they limit policy representation to parametric…
continuous-controlContinuous ControlPolicy Gradient MethodsTrajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estim…
continuous-controlContinuous ControlDeep Reinforcement LearningPolicy Gradient Methods+4Learning Novel Policies For Tasks
In this work, we present a reinforcement learning algorithm that can find a variety of policies (novel policies) for a task that is given by a task reward function. Our method does this by creating a second reward functi…
Policy Gradient MethodsReinforcement LearningObject Exchangeability in Reinforcement Learning: Extended Abstract
Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge. Careful choice of input representations can help improve efficiency depending on t…
Deep Reinforcement LearningObjectPolicy Gradient Methodsreinforcement-learning+2Neural Logic Reinforcement Learning
Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalizing the learned policy which makes the learning performance largely aff…
Deep Reinforcement LearningInductive logic programmingPolicy Gradient Methodsreinforcement-learning+2Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)
Reinforcement learning (RL) is about sequential decision making and is traditionally opposed to supervised learning (SL) and unsupervised learning (USL). In RL, given the current state, the agent makes a decision that ma…
Decision MakingPolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)+1Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are based on direct policy sampling, self-p…
continuous-controlContinuous ControlDenoisingPolicy Gradient Methods+1StartNet: Online Detection of Action Start in Untrimmed Videos
We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos. Previous methods aim to localize action starts by le…
Action ClassificationPolicy Gradient MethodsEvaluating Rewards for Question Generation Models
Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. Models are trained using teacher forcing to optimise only the one-step-ahead predict…
Machine TranslationPolicy Gradient MethodsQuestion GenerationQuestion-Generation+2