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Found 4 papers, 2 papers with code
Unsupervised Learning of Effective Actions in Robotics
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics.
Colored Noise in PPO: Improved Exploration and Performance Through Correlated Action Sampling
Proximal Policy Optimization (PPO), a popular on-policy deep reinforcement learning method, employs a stochastic policy for exploration.
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.
Continual Learning from Demonstration of Robotics Skills
We empirically demonstrate the effectiveness of this approach in remembering long sequences of trajectory learning tasks without the need to store any data from past demonstrations.
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