GenRL is a PyTorch reinforcement learning library centered around reproducible, generalizable algorithm implementations and improving accessibility in Reinforcement Learning. GenRL's current release is at v0.0.2. Expect breaking changes. Reinforcement learning research is moving faster than ever before.

Using Shared Parameters in Actor Critic Agents in GenRL; Vanilla Policy Gradient (VPG) Saving and Loading Weights and Hyperparameters with GenRL

Jun 26, 2024 · The resulting agent learning framework, GenRL, allows one to specify tasks through vision and/or language prompts, ground them in the embodied domain's dynamics, and learn the corresponding behaviors in imagination.

Bases: genrl.core.values.MlpValue. reset_noise → None [source] ¶ Resets noise for any Noisy layers in Value function. class genrl.core.values.MlpValue (state_dim: int, action_dim: int = None, val_type: str = 'V', fc_layers: Tuple = (32, 32), **kwargs) [source] ¶ Bases: genrl.core.base.BaseValue

GenRL is compatible with Python 3.6 or later and also depends on pytorch and openai-gym. The easiest way to install GenRL is with pip, Python’s preferred package installer.

GenRL: Multimodal-foundation world models for generalization in embodied agents Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron Courville, Sai Rajeswar

In this work, we present GenRL, a novel approach requiring no language annotations that allows training agents to solve multiple tasks from visual or language prompts.

[NeurIPS 2024] GenRL: Multimodal-foundation world models enable grounding language and video prompts into embodied domains, by turning them into sequences of latent world model states.

Aug 27, 2020 · Reinforcement learning research is moving faster than ever before. In order to keep up with the growing trend and ensure that RL research remains reproducible, GenRL aims to aid faster paper reproduction and benchmarking by providing the following main features:

GenRL is a PyTorch reinforcement learning library centered around reproducible and generalizable algorithm implementations. I am currently integrating various Deep and Classical RL agents such as DQN, VPG and SARSA to GenRL. GenRL aims to aid faster paper reproduction and benchmarking by providing the following main features: