69 citations · 111 across the 27 of their papers we have counts for
13 papers · 1 filter
Leveraging Human Feedback for Semantically-Relevant Skill Discovery
Maxence Hussonnois, Thommen George Karimpanal, Santu Rana
Unsupervised skill discovery in reinforcement learning aims to intrinsically motivate agents to discover diverse and useful behaviours. However, unconstrained approaches can produc…
Human-Aligned Skill Discovery: Balancing Behaviour Exploration and Alignment
Maxence Hussonnois, Thommen George Karimpanal, Santu Rana
Unsupervised skill discovery in Reinforcement Learning aims to mimic humans' ability to autonomously discover diverse behaviors. However, existing methods are often unconstrained,…
Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired Skills
Maxence Hussonnois, Thommen George Karimpanal, Santu Rana
Autonomously learning diverse behaviors without an extrinsic reward signal has been a problem of interest in reinforcement learning. However, the nature of learning in such mechani…
Zero-shot Sim2Real Adaptation Across Environments
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Simulation based learning often provides a cost-efficient recourse to reinforcement learning applications in robotics. However, simulators are generally incapable of accurately rep…
Uncertainty Aware System Identification with Universal Policies
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real tr…
Fast Model-based Policy Search for Universal Policy Networks
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal poli…
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…
Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning
Thommen George Karimpanal, Roland Bouffanais
The idea of reusing information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample effi…