3 accepted papers
Categorical Distributional Reinforcement Learning (CDRL) has demonstrated superior sample efficiency in learning complex tasks compared to conventional Reinforcement Learning (RL) approaches. However, the practical application of CDRL is encumbered by challenging projection steps, detailed parameter…
Reinforcement Learning (RL) has been widely used to solve tasks where the environment consistently provides a dense reward value. However, in real-world scenarios, rewards can often be poorly defined or sparse. Auxiliary signals are indispensable for discovering efficient exploration strategies and…
Model Free Reinforcement Learning (MFRL) has shown significant promise for learning dexterous robotic manipulation tasks, at least in simulation. However, the high number of samples, as well as the long training times, prevent MFRL from scaling to complex real-world tasks. Model- Based Reinforcement…