Behavioural cloning
11 papers with code • 0 benchmarks • 2 datasets
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Latest papers with no code
Model-based trajectory stitching for improved behavioural cloning and its applications
Furthermore, using the D4RL benchmarking suite, we demonstrate that state-of-the-art results are obtained by combining TS with two existing offline learning methodologies reliant on BC, model-based offline planning (MBOP) and policy constraint (TD3+BC).
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning
We propose a model-based data augmentation strategy, Trajectory Stitching (TS), to improve the quality of sub-optimal historical trajectories.
Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning
The ability to discover optimal behaviour from fixed data sets has the potential to transfer the successes of reinforcement learning (RL) to domains where data collection is acutely problematic.
Information-Theoretic Policy Learning from Partial Observations with Fully Informed Decision Makers
In this work we formulate and treat an extension of the Imitation from Observations problem.
Improving Behavioural Cloning with Human-Driven Dynamic Dataset Augmentation
Behavioural cloning has been extensively used to train agents and is recognized as a fast and solid approach to teach general behaviours based on expert trajectories.
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network
Against this background, we introduce a simulation based online planning algorithm, that we call SiCLOP, for multi-agent cooperative environments.
Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets
Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments.
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems?
Semi-supervised reward learning for offline reinforcement learning
In offline reinforcement learning (RL) agents are trained using a logged dataset.
Offline Reinforcement Learning Hands-On
Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment.
