Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning

--We introduce Unsupervised Meta-T esting with Conditional Neural Processes (UMCNP), a novel hybrid few-shot meta-reinforcement learning (meta-RL) method that uniquely combines, yet distinctly separates, parameterized policy gradient-based (PPG) and task inference-based few-shot meta-RL. T ai-lored for settings where the reward signal is missing during meta-testing, our method increases sample efficiency without requiring additional samples in meta-training. UMCNP leverages the efficiency and scalability of Conditional Neural Processes (CNPs) to reduce the number of online interactions required in meta-testing. During meta-training, samples previously collected through PPG meta-RL are efficiently reused for learning task inference in an offline manner . This approach allows us to generate rollouts for self-adaptation by interacting with the learned dynamics model. We demonstrate our method can adapt to an unseen test task using significantly fewer samples during meta-testing than the baselines in 2D-Point Agent and continuous control meta-RL benchmarks, namely, cartpole with unknown angle sensor bias, walker agent with randomized dynamics parameters. ESPITE the remarkable achievements of deep reinforcement learning (RL) algorithms [1], [2], they are hindered by poor sample efficiency and limited generalizability. Few-shot meta-reinforcement learning (meta-RL) framework [3], [4] aims to overcome these limitations by learning a distribution of tasks during meta-training and adapting to a new task from the same distribution using a few samples from the new task in meta-testing.