mirror of
https://github.com/huggingface/deep-rl-class.git
synced 2026-05-01 22:30:15 +08:00
27 lines
2.2 KiB
Plaintext
27 lines
2.2 KiB
Plaintext
# Model Based Reinforcement Learning
|
||
|
||
# Model-based reinforcement learning (MBRL)
|
||
|
||
Model-based reinforcement learning only differs from it’s model-free counterpart in the learning of a *dynamics model*, but that has substantial downstream effects on how the decisions are made.
|
||
The dynamics models most often model the environment transition dynamics, \\( s_{t+1} = f_\theta (s_t, a_t) \\), but things like inverse dynamics models (mapping from states to actions) or reward models (predicting rewards) can be used in this framework.
|
||
|
||
**Simple version**:
|
||
|
||
There is an agent that repeatedly tries to solve a problem, accumulating state and action data.
|
||
With that data, the agent creates a structured learning tool -- a dynamics model -- to reason about the world.
|
||
With the dynamics model, the agent decides how to act by predicting into the future.
|
||
With those actions, the agent collects more data, improves said model, and hopefully improves future actions.
|
||
|
||
**Academic version**:
|
||
|
||
Model-based reinforcement learning (MBRL) follows the framework of an agent interacting in an environment, learning a model of said environment, and then leveraging the model for control.
|
||
Specifically, the agent acts in a Markov Decision Process (MDP) governed by a transition function \\( s_{t+1} = f (s_t , a_t) \\) and returns a reward at each step \\( r(s_t, a_t) \\). With a collected dataset \\( D :={ s_i, a_i, s_{i+1}, r_i} \\), the agent learns a model, \\( s_{t+1} = f_\theta (s_t , a_t) \\) to minimize the negative log-likelihood of the transitions.
|
||
We employ sample-based model-predictive control (MPC) using the learned dynamics model, which optimizes the expected reward over a finite, recursively predicted horizon, \\( \tau \\), from a set of actions sampled from a uniform distribution \\( U(a) \\), (see [paper](https://arxiv.org/pdf/2002.04523) or [paper](https://arxiv.org/pdf/2012.09156.pdf) or [paper](https://arxiv.org/pdf/2009.01221.pdf)).
|
||
|
||
## Further reading
|
||
For more information on MBRL, we recommend you check out the following resources.
|
||
|
||
1. A [recent review paper on MBRL (long)](https://arxiv.org/abs/2006.16712),
|
||
2. A [blog post on debugging MBRL](https://www.natolambert.com/writing/debugging-mbrl).
|
||
|