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Residual policy learning facilitates efficient model-free autonomous racing
Motion planning for autonomous racing is a challenging task due to the safety requirement
while driving aggressively. Most previous solutions utilize the prior information or depend on …
while driving aggressively. Most previous solutions utilize the prior information or depend on …
Action noise in off-policy deep reinforcement learning: Impact on exploration and performance
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration
such as the additive action noise often used in continuous control domains. Typically, the …
such as the additive action noise often used in continuous control domains. Typically, the …
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 …
methods, simpler critic-only methods such as Q-learning find limited application in the …
Latent imagination facilitates zero-shot transfer in autonomous racing
World models learn behaviors in a latent imagination space to enhance the sample-
efficiency of deep reinforcement learning (RL) algorithms. While learning world models for …
efficiency of deep reinforcement learning (RL) algorithms. While learning world models for …
Continuous control with coarse-to-fine reinforcement learning
Despite recent advances in improving the sample-efficiency of reinforcement learning (RL)
algorithms, designing an RL algorithm that can be practically deployed in real-world …
algorithms, designing an RL algorithm that can be practically deployed in real-world …
Continuous control with action quantization from demonstrations
In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with
continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The …
continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The …
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. The underlying coarse …
bang-bang policies for solving continuous control benchmarks. The underlying coarse …
Reinforcement learning with simple sequence priors
In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis--
but this timescale ignores temporal regularities, like repetitions, often present in sequential …
but this timescale ignores temporal regularities, like repetitions, often present in sequential …
Distributional reinforcement learning-based energy arbitrage strategies in imbalance settlement mechanism
Growth in the penetration of renewable energy sources makes supply more uncertain and
leads to an increase in the system imbalance. This trend, together with the single imbalance …
leads to an increase in the system imbalance. This trend, together with the single imbalance …
Geometric fabrics: a safe guiding medium for policy learning
Robotics policies are always subjected to complex, second order dynamics that entangle
their actions with resulting states. In reinforcement learning (RL) contexts, policies have the …
their actions with resulting states. In reinforcement learning (RL) contexts, policies have the …