Deep Reinforcement Learning with Surrogate Agent-Environment Interface

2017-09-12 · Song Wang, Yu Jing

In this paper, we propose surrogate agent-environment interface (SAEI) in reinforcement learning. We also state that learning based on probability surrogate agent-environment interface provides optimal policy of task agent-environment interface. We introduce surrogate probability action and develop the probability surrogate action deterministic policy gradient (PSADPG) algorithm based on SAEI. This algorithm enables continuous control of discrete action. The experiments show PSADPG achieves the performance of DQN in certain tasks with the stochastic optimal policy nature in the initial training stage.

📄 PDF Abstract BibTeX arXiv:1709.03942

Code (0)

등록된 구현이 없습니다.

Tasks

continuous-control Continuous ControlDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

DQN A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari…

Godot Reinforcement Learning Agents

2021-12-07 · Edward Beeching, Jilles Debangoye, Olivier Simonin, Christian Wolf

We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine. The Godot RL Agents interface allows the design, creation and learning of age…

CPU reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Building surrogate models using trajectories of agents trained by Reinforcement Learning

2025-09-01 · Julen Cestero, Marco Quartulli, Marcello Restelli arxiv

Sample efficiency in the face of computationally expensive simulations is a common concern in surrogate modeling. Current strategies to minimize the number of samples needed are not as effective in simulated environments…

Reinforcement Learning Active Learning

Deep Surrogate Assisted Generation of Environments

2022-06-09 · Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine, Stefanos Nikolaidis

Recent progress in reinforcement learning (RL) has started producing generally capable agents that can solve a distribution of complex environments. These agents are typically tested on fixed, human-authored environments…

Diversity Reinforcement Learning (RL)

Arena: a toolkit for Multi-Agent Reinforcement Learning

2019-07-20 · Qing Wang, Jiechao Xiong, Lei Han, Meng Fang 외

We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research. In MARL, it usually requires customizing observations, rewards and actions for each agent, changing cooperative-competitive agent-inte…

Multi-agent Reinforcement Learning OpenAI Gym reinforcement-learning Reinforcement Learning+3

Unified Distributed Environment

2022-05-14 · Woong Gyu La, Sunil Muralidhara, Lingjie Kong, Pratik Nichat

We propose Unified Distributed Environment (UDE), an environment virtualization toolkit for reinforcement learning research. UDE is designed to integrate environments built on any simulation platform such as Gazebo, Unit…

OpenAI Gym reinforcement-learning Reinforcement Learning (RL)Unity