Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
Designing a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed within the Markov decision process (MDP) framework. This paper presents …
FormPolicy Gradient MethodsReinforcement Learning for Causal Discovery without Acyclicity Constraints
Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAGs) from observational data. However, the…
Causal DiscoveryEfficient ExplorationNavigatePolicy Gradient Methods+3Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs
We study the problem of computing deterministic optimal policies for constrained Markov decision processes (MDPs) with continuous state and action spaces, which are widely encountered in constrained dynamical systems. De…
continuous-controlContinuous ControlPolicy Gradient MethodsRobot NavigationFrom Imitation to Refinement -- Residual RL for Precise Assembly
Recent advances in Behavior Cloning (BC) have made it easy to teach robots new tasks. However, we find that the ease of teaching comes at the cost of unreliable performance that saturates with increasing data for tasks r…
ChunkingPolicy Gradient MethodsReinforcement Learning (RL)PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods
This paper introduces PG-Rainbow, a novel algorithm that incorporates a distributional reinforcement learning framework with a policy gradient algorithm. Existing policy gradient methods are sample inefficient and rely o…
Atari GamesDecision MakingDistributional Reinforcement LearningPolicy Gradient Methods+2Towards Adapting Reinforcement Learning Agents to New Tasks: Insights from Q-Values
While contemporary reinforcement learning research and applications have embraced policy gradient methods as the panacea of solving learning problems, value-based methods can still be useful in many domains as long as we…
Policy Gradient Methodsreinforcement-learningReinforcement LearningAugmented Bayesian Policy Search
Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement and interpret. However, in practice, ex…
Bayesian OptimizationLEMMAPolicy Gradient Methodsreinforcement-learning+1Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions
Recently proposed reward-conditioned policies (RCPs) offer an appealing alternative in reinforcement learning. Compared with policy gradient methods, policy learning in RCPs is simpler since it is based on supervised lea…
Multi-Armed BanditsPolicy Gradient MethodsThompson SamplingCurrent applications and potential future directions of reinforcement learning-based Digital Twins in agriculture
Digital Twins have gained attention in various industries for simulation, monitoring, and decision-making, relying on ever-improving machine learning models. However, agricultural Digital Twin implementations are limited…
Decision MakingManagementPolicy Gradient Methodsreinforcement-learning+1Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes
We study the error introduced by entropy regularization in infinite-horizon, discrete, discounted Markov decision processes. We show that this error decreases exponentially in the inverse regularization strength both in …
Policy Gradient MethodsEntropy annealing for policy mirror descent in continuous time and space
Entropy regularization has been widely used in policy optimization algorithms to enhance exploration and the robustness of the optimal control; however it also introduces an additional regularization bias. This work quan…
Policy Gradient MethodsMollification Effects of Policy Gradient Methods
Policy gradient methods have enabled deep reinforcement learning (RL) to approach challenging continuous control problems, even when the underlying systems involve highly nonlinear dynamics that generate complex non-smoo…
continuous-controlContinuous ControlDeep Reinforcement LearningPolicy Gradient Methods+1Matrix Low-Rank Approximation For Policy Gradient Methods
Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but exact VF computation suffers from the cur…
Matrix CompletionPolicy Gradient MethodsLinear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges
Neural Network based approximations of the Value function make up the core of leading Policy Based methods such as Trust Regional Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). While this adds signifi…
AcrobotPolicy Gradient MethodsPolicy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL methods aim to learn a point estimate of the random cumulative cost, distributi…
Distributional Reinforcement LearningPolicy Gradient MethodsReinforcement Learning (RL)Almost sure convergence rates of stochastic gradient methods under gradient domination
Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow a simple analysis they are rarely satisfied in applica…
Policy Gradient Methodsreinforcement-learningReinforcement LearningAn Initial Introduction to Cooperative Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. While numerous approaches have been developed, they can be broadly categorized into three main types: centralized training and executi…
MisconceptionsMulti-agent Reinforcement LearningPolicy Gradient MethodsQ-Learning+2Federated Reinforcement Learning with Constraint Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while $N$ training agents are located …
Language ModelingLanguage ModellingLarge Language ModelPolicy Gradient Methods+2Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline
Policy-based methods have achieved remarkable success in solving challenging reinforcement learning problems. Among these methods, off-policy policy gradient methods are particularly important due to that they can benefi…
Computational EfficiencyMuJoCoOpenAI GymPolicy Gradient MethodsInformation-Theoretic Opacity-Enforcement in Markov Decision Processes
The paper studies information-theoretic opacity, an information-flow privacy property, in a setting involving two agents: A planning agent who controls a stochastic system and an observer who partially observes the syste…
Policy Gradient Methods