Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration
To achieve scenario intelligence, humans must transfer knowledge to robots by developing goal-oriented algorithms, which are sometimes insensitive to dynamically changing environments. While deep reinforcement learning a…
Deep Reinforcement LearningEfficient ExplorationHierarchical Reinforcement LearningPolicy Gradient Methods+3Remember and Forget for Experience Replay
Experience replay (ER) is a fundamental component of off-policy deep reinforcement learning (RL). ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficien…
Deep Reinforcement LearningPolicy Gradient MethodsQ-LearningReinforcement Learning+1Variance Reduction for Reinforcement Learning in Input-Driven Environments
We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, rob…
Meta-LearningMuJoCoObject TrackingPolicy Gradient Methods+3Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end up focusing on simple utterances and su…
Deep Reinforcement LearningPolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)+1Policy Optimization with Demonstrations
Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse. Recent methods of learning from demonstrations have shown to be promising in…
Policy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Focused Hierarchical RNNs for Conditional Sequence Processing
Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most of these models use a simple form of encoder with attention that looks over the e…
Open-Domain Question AnsweringPolicy Gradient MethodsQuestion AnsweringFingerprint Policy Optimisation for Robust Reinforcement Learning
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulato…
Bayesian OptimisationContinuous ControlPolicy Gradient Methodsreinforcement-learning+2Learning Self-Imitating Diverse Policies
The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-m…
continuous-controlContinuous ControlDecision MakingDeep Reinforcement Learning+6Multiagent Soft Q-Learning
Policy gradient methods are often applied to reinforcement learning in continuous multiagent games. These methods perform local search in the joint-action space, and as we show, they are susceptable to a game-theoretic p…
Policy Gradient MethodsQ-Learningreinforcement-learningReinforcement Learning+1On Learning Intrinsic Rewards for Policy Gradient Methods
In many sequential decision making tasks, it is challenging to design reward functions that help an RL agent efficiently learn behavior that is considered good by the agent designer. A number of different formulations of…
Atari GamesDecision MakingMuJoCoPolicy Gradient Methods+1Information Maximizing Exploration with a Latent Dynamics Model
All reinforcement learning algorithms must handle the trade-off between exploration and exploitation. Many state-of-the-art deep reinforcement learning methods use noise in the action selection, such as Gaussian noise in…
continuous-controlContinuous ControlDeep Reinforcement Learningmodel+5Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons …
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1The Mirage of Action-Dependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Optimizing over a Restricted Policy Class in Markov Decision Processes
We address the problem of finding an optimal policy in a Markov decision process under a restricted policy class defined by the convex hull of a set of base policies. This problem is of great interest in applications in …
Policy Gradient MethodsAsynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools and techniques that are popular in multi-agent and distributed control scenarios. To counter Bellman's curse of dimensio…
Multi-agent Reinforcement LearningPolicy Gradient MethodsClipped Action Policy Gradient
Many continuous control tasks have bounded action spaces. When policy gradient methods are applied to such tasks, out-of-bound actions need to be clipped before execution, while policies are usually optimized as if the a…
continuous-controlContinuous ControlPolicy Gradient MethodsPolicy Gradients for Contextual Recommendations
Decision making is a challenging task in online recommender systems. The decision maker often needs to choose a contextual item at each step from a set of candidates. Contextual bandit algorithms have been successfully d…
Decision MakingMulti-Armed BanditsPolicy Gradient MethodsRecommendation SystemsGlobal Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
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 LearningExpected Policy Gradients for Reinforcement Learning
We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across a…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Action-dependent Control Variates for Policy Optimization via Stein 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)