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Robot Learning

Policy architectures for robot manipulation and imitation learning.

World Models and Model-Based RL

Modules for model-based reinforcement learning, including world models and dynamics models. For an overview of how the DreamerV3 components fit together, see DreamerV3 in a nutshell.

WorldModel(*args, **kwargs)	A general, composable world model for model-based RL.
WorldModelWrapper(*args, **kwargs)	World model wrapper.
DreamerActor(out_features[, depth, ...])	Dreamer actor network.
ObsEncoder([channels, num_layers, ...])	Observation encoder network.
ObsDecoder([channels, num_layers, ...])	Observation decoder network.
RSSMPosterior([hidden_dim, state_dim, ...])	The posterior network of the RSSM.
RSSMPrior(action_spec[, hidden_dim, ...])	The prior network of the RSSM.
RSSMRollout(*args, **kwargs)	Rollout the RSSM network.
SymExpTwoHot([num_bins])	DreamerV3 categorical scalar representation.
DreamerV3MLP(in_features, out_features[, ...])	RMS-normalized multilayer perceptron used by DreamerV3 heads.

PILCO

Components for moment-matching model-based policy search (PILCO).

GPWorldModel(obs_dim, action_dim[, in_keys, ...])	Gaussian Process world model with moment-matching uncertainty propagation.
RBFController(input_dim, output_dim, max_action)	Radial Basis Function controller for moment-matching policy search.