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Found 7 papers, 4 papers with code
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task.
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks.
Model-based Reinforcement Learning
reinforcement-learning
+2
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control
In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives the reward corresponding to that action by a delay called action-effect delay.
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO
Different versions of two actor-critic learning algorithms are tested on this environment: Sampled Policy Gradient (SPG) and Proximal Policy Optimization (PPO).
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms.
Deep Quality-Value (DQV) Learning
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning.
Sampled Policy Gradient for Learning to Play the Game Agar.io
In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG).
