model-based-rl

World models for planning in model-based reinforcement learning involve learning a dynamics model from real environment interactions, then simulating short rollouts (k in 5-10) to bootstrap value functions and optimize policies. This skill provides MBPO and Dreamer templates for this workflow.

The difference is that Dyna-Q integrates model-free learning with simulated planning, MBPO uses an ensemble of dynamics models for short-horizon rollouts with uncertainty checks, and Dreamer uses a learned world model for latent imagination-based policy optimization.

Implementing MBPO constraints with ensemble uncertainty checks requires training an ensemble of dynamics models, enforcing short rollouts (k in 5-10 steps), and using ensemble disagreement to measure uncertainty for bootstrapping the value function during policy optimization.

Yes, model-based RL can be used for sim-to-real transfer across common benchmarks by training world models in simulation and adapting policies to real environments. This skill offers templates and pseudocode to guide sim-to-real transfer experimentation.

Model-based RL methods use short rollouts with k in 5 to 10 steps to prevent compounding model errors from degrading the learned value function. MBPO-style constraints limit imagination horizons to keep bootstrapped estimates reliable and stable.

MPC planning works with stochastic world models in model-based RL by using sampling-based optimizers like CEM (Cross-Entropy Method) to handle uncertainty. This skill covers planning with both deterministic and stochastic dynamics models.