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Found 19 papers, 3 papers with code
Redeeming Intrinsic Rewards via Constrained Optimization
However, on easy exploration tasks, the agent gets distracted by intrinsic rewards and performs unnecessary exploration even when sufficient task (also called extrinsic) reward is available.
Model Predictive Control via On-Policy Imitation Learning
Recent approaches to data-driven MPC have used the simplest form of imitation learning known as behavior cloning to learn controllers that mimic the performance of MPC by online sampling of the trajectories of the closed-loop MPC system.
Bilinear value networks
The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function.
Topological Experience Replay
State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer.
Stubborn: A Strong Baseline for Indoor Object Navigation
We present a strong baseline that surpasses the performance of previously published methods on the Habitat Challenge task of navigating to a target object in indoor environments.
Bi-linear Value Networks for Multi-goal Reinforcement Learning
We simultaneously learn both components.
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks.
Toward Synergism in Macro Action Ensembles
Macro actions have been demonstrated to be beneficial for the learning processes of an agent.
Mixture of Step Returns in Bootstrapped DQN
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning
PIEKD is a learning framework that uses an ensemble of policies to act in the environment while periodically sharing knowledge amongst policies in the ensemble through knowledge distillation.
Collaborative Inter-agent Knowledge Distillation for Reinforcement Learning
Reinforcement Learning (RL) has demonstrated promising results across several sequential decision-making tasks.
Model-based Lookahead Reinforcement Learning
Model-based Reinforcement Learning (MBRL) allows data-efficient learning which is required in real world applications such as robotics.
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.
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode.
