Recomposing the Reinforcement Learning Building Blocks with Hypernetworks

• URL: http://arxiv.org/abs/2106.06842v1
• Date: Sat, 12 Jun 2021 19:43:12 GMT
• Title: Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
• Authors: Shai Keynan, Elad Sarafian and Sarit Kraus
• Abstract summary: We show that a primary network determines the weights of a conditional dynamic network. This approach improves the gradient approximation and reduces the learning step variance. We demonstrate a consistent improvement across different locomotion tasks and different algorithms both in RL (TD3 and SAC) and in Meta-RL (MAML and PEARL)
• Score: 19.523737925041278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Reinforcement Learning (RL) building blocks, i.e. Q-functions and policy networks, usually take elements from the cartesian product of two domains as input. In particular, the input of the Q-function is both the state and the action, and in multi-task problems (Meta-RL) the policy can take a state and a context. Standard architectures tend to ignore these variables' underlying interpretations and simply concatenate their features into a single vector. In this work, we argue that this choice may lead to poor gradient estimation in actor-critic algorithms and high variance learning steps in Meta-RL algorithms. To consider the interaction between the input variables, we suggest using a Hypernetwork architecture where a primary network determines the weights of a conditional dynamic network. We show that this approach improves the gradient approximation and reduces the learning step variance, which both accelerates learning and improves the final performance. We demonstrate a consistent improvement across different locomotion tasks and different algorithms both in RL (TD3 and SAC) and in Meta-RL (MAML and PEARL).

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