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Found 9 papers, 3 papers with code
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks.
Towards Cooperative Flight Control Using Visual-Attention
We propose an air-guardian system that facilitates cooperation between a pilot with eye tracking and a parallel end-to-end neural control system.
Solving Continuous Control via Q-learning
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces.
Interpreting Neural Policies with Disentangled Tree Representations
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning.
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents.
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space.
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
Learning complex robot behaviors through interaction requires structured exploration.
Learning to Plan via Deep Optimistic Value Exploration
Deep exploration requires coordinated long-term planning.
