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Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics of the environment or external rewards. To tackle these challenges, thi
Externí odkaz:
http://arxiv.org/abs/2310.19261
Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensio
Externí odkaz:
http://arxiv.org/abs/2310.17330
While reinforcement learning (RL) has achieved great success in acquiring complex skills solely from environmental interactions, it assumes that resets to the initial state are readily available at the end of each episode. Such an assumption hinders
Externí odkaz:
http://arxiv.org/abs/2305.09943
Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed. Even though curriculum RL, a framework that solves complex tasks by proposing a sequence
Externí odkaz:
http://arxiv.org/abs/2301.11741
Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from
Externí odkaz:
http://arxiv.org/abs/2209.15256
Publikováno v:
IEEE Robotics and Automation Letters 7 (2022) 7455-7462
Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm. To alleviate it, unsupervised RL, a framework that pre-trains the agent in a task-
Externí odkaz:
http://arxiv.org/abs/2204.13906
Publikováno v:
IEEE Robotics and Automation Letters 7 (2022) 6606-6613
Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning algorithms assume an episodic setting, in which the agent resets to a fi
Externí odkaz:
http://arxiv.org/abs/2204.02041
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