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Found 7 papers, 0 papers with code
Reusability and Transferability of Macro Actions for Reinforcement Learning
However, by using a proper macro action, defined as a sequence of primitive actions, an agent is able to bypass intermediate states to a farther state and facilitate its learning procedure.
A Self-Supervised Method for Mapping Human Instructions to Robot Policies
In this paper, we propose a modular approach which separates the instruction-to-action mapping procedure into two separate stages.
Adversarial Exploration Strategy for Self-Supervised Imitation Learning
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
Adversarial Active Exploration for Inverse Dynamics Model Learning
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards.
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform.
A Deep Policy Inference Q-Network for Multi-Agent Systems
DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learning models.
