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Found 14 papers, 2 papers with code
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying
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.
Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired Skills
Autonomously learning diverse behaviors without an extrinsic reward signal has been a problem of interest in reinforcement learning.
Zero-shot Sim2Real Adaptation Across Environments
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.
Uncertainty Aware System Identification with Universal Policies
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments.
Fast Model-based Policy Search for Universal Policy Networks
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning.
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard $Q-$learning.
Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation.
Neuro-evolutionary Frameworks for Generalized Learning Agents
The recent successes of deep learning and deep reinforcement learning have firmly established their statuses as state-of-the-art artificial learning techniques.
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent.
Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning
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.
Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning
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.
A Self-Replication Basis for Designing Complex Agents
In this work, we describe a self-replication-based mechanism for designing agents of increasing complexity.
Experience Replay Using Transition Sequences
Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms.
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering
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.
