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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.
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.
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