69 citations · 111 across the 27 of their papers we have counts for
6 papers · 1 filter
Episodic Policy Gradient Training
Hung Le, Majid Abdolshah, Thommen K. George +3
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.…
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Hung Le, Thommen Karimpanal George, Majid Abdolshah +2
Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory. We propose a new model-based episodic memory of trajecto…
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets
Thommen George Karimpanal, Hung Le, Majid Abdolshah +4
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard learning. Such a bias fails to account for…
Plug and Play, Model-Based Reinforcement Learning
Majid Abdolshah, Hung Le, Thommen Karimpanal George +3
Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce P…
A New Representation of Successor Features for Transfer across Dissimilar Environments
Majid Abdolshah, Hung Le, Thommen Karimpanal George +3
Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function ch…
Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation. However, such…