Papers OpenAI Gym
“OpenAI Gym” 태그가 달린 논문 406편 · 필터 해제
SPADE: Self-Play in Adaptive Synthetic Executable Environments
Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) …
OpenAI GymProDVI: Programmatic Dynamics Priors for Value Network Initialization
Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch, forcing them to acquire task-relevant knowledge through online interac…
Reinforcement LearningOpenAI GymLagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift. Direct reinforcement learning fine-tuning can improve …
Reinforcement LearningOpenAI GymReflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant Markov Decision Processes ($G$-invariant MDPs). Existing works in this …
Reinforcement LearningContinuous ControlOpenAI GymInterpretable experiential learning based on state history and global feedback
A new interpretable experiential learning model based on state history and global feedback is presented. It is capable of learning a behavioral model represented by a transition graph between sets of states, with transit…
Reinforcement LearningOpenAI GymBitboard version of Tetris AI
The efficiency of game engines and policy optimization algorithms is crucial for training reinforcement learning (RL) agents in complex sequential decision-making tasks, such as Tetris. Existing Tetris implementations su…
Reinforcement LearningOpenAI GymRobust Transfer Learning with Side Information
Robust Markov Decision Processes (MDPs) address environmental shift through distributionally robust optimization (DRO) by finding an optimal worst-case policy within an uncertainty set of transition kernels. However, sta…
Transfer LearningOpenAI GymDirect Soft-Policy Sampling via Langevin Dynamics
Soft policies in reinforcement learning define policies as Boltzmann distributions over state-action value functions, providing a principled mechanism for balancing exploration and exploitation. However, realizing such s…
Reinforcement LearningOpenAI GymOne Step Is Enough: Dispersive MeanFlow Policy Optimization
Real-time robotic control demands fast action generation. However, existing generative policies based on diffusion and flow matching require multi-step sampling, fundamentally limiting deployment in time-critical scenari…
Knowledge DistillationReinforcement LearningOpenAI GymFormulating Reinforcement Learning for Human-Robot Collaboration through Off-Policy Evaluation
Reinforcement learning (RL) has the potential to transform real-world decision-making systems by enabling autonomous agents to learn from experience. Deploying RL in real-world settings, especially in the context of huma…
Reinforcement LearningOffline RLOpenAI GymProximal Policy Optimization with Evolutionary Mutations
Proximal Policy Optimization (PPO) is a widely used reinforcement learning algorithm known for its stability and sample efficiency, but it often suffers from premature convergence due to limited exploration. In this pape…
Reinforcement LearningOpenAI GymReward Learning through Ranking Mean Squared Error
Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems. A popular alternative is reward learning, where reward functions are inferred from human feedback rather than…
Reinforcement LearningOpenAI GymKnow your Trajectory -- Trustworthy Reinforcement Learning deployment through Importance-Based Trajectory Analysis
As Reinforcement Learning (RL) agents are increasingly deployed in real-world applications, ensuring their behavior is transparent and trustworthy is paramount. A key component of trust is explainability, yet much of the…
Reinforcement LearningOpenAI GymEnhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay
Background: Deep Deterministic Policy Gradient-based reinforcement learning algorithms utilize Actor-Critic architectures, where both networks are typically trained using identical batches of replayed transitions. Howeve…
Reinforcement LearningContinuous ControlOpenAI GymA Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms
This paper presents a quantitative comparison between centralised and distributed multi-agent reinforcement learning (MARL) architectures for controlling a soft robotic arm modelled as a Cosserat rod in simulation. Using…
Multi-agent Reinforcement LearningOpenAI GymOff-policy Reinforcement Learning with Model-based Exploration Augmentation
Exploration is fundamental to reinforcement learning (RL), as it determines how effectively an agent discovers and exploits the underlying structure of its environment to achieve optimal performance. Existing exploration…
Reinforcement LearningOpenAI GymMind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further performance improvement: (1) non-stationar…
Reinforcement LearningOpenAI GymAdversarial Reinforcement Learning for Offensive and Defensive Agents in a Simulated Zero-Sum Network Environment
This paper presents a controlled study of adversarial reinforcement learning in network security through a custom OpenAI Gym environment that models brute-force attacks and reactive defenses on multi-port services. The e…
Reinforcement LearningTransfer LearningOpenAI GymA Forensic Analysis of Synthetic Data in RL: Diagnosing and Solving Algorithmic Failures in Model-Based Policy Optimization
Synthetic data is central to data-efficient Dyna-style model-based reinforcement learning, but it can also degrade performance. We study this failure in Model-Based Policy Optimization (MBPO), which performs actor-critic…
Reinforcement LearningContinuous ControlOpenAI GymImitate Optimal Policy: Prevail and Induce Action Collapse in Policy Gradient
Policy gradient (PG) methods in reinforcement learning frequently utilize deep neural networks (DNNs) to learn a shared backbone of feature representations used to compute likelihoods in an action selection layer. Numero…
Reinforcement LearningOpenAI Gym