1 code implementation • 21 Aug 2023 • Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana, Hung Le, Truyen Tran, Sunil Gupta, Svetha Venkatesh
To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions.
1 code implementation • 7 Mar 2023 • 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.
no code implementations • 8 Feb 2023 • Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh
However, simulators are generally incapable of accurately replicating real-world dynamics, and thus bridging the sim2real gap is an important problem in simulation based learning.
no code implementations • 11 Feb 2022 • Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments.
no code implementations • 11 Feb 2022 • Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning.
no code implementations • 3 Nov 2021 • Thommen George Karimpanal, Hung Le, Majid Abdolshah, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard $Q-$learning.
no code implementations • 18 Apr 2021 • Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation.
no code implementations • 4 Feb 2020 • Thommen George Karimpanal
The recent successes of deep learning and deep reinforcement learning have firmly established their statuses as state-of-the-art artificial learning techniques.
no code implementations • 10 Sep 2019 • Thommen George Karimpanal, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh
Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent.
no code implementations • 18 Nov 2018 • Thommen George Karimpanal, Roland Bouffanais
In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks.
no code implementations • 19 Jul 2018 • 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 efficiency reinforcement learning agents.
no code implementations • 18 May 2018 • Thommen George Karimpanal
In this work, we describe a self-replication-based mechanism for designing agents of increasing complexity.
no code implementations • 30 May 2017 • Thommen George Karimpanal, Roland Bouffanais
Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms.
no code implementations • 17 May 2017 • Thommen George Karimpanal, Erik Wilhelm
In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and learning them in parallel.