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No TD Studying, Benefit Reweighting, or Transformers – The Berkeley Synthetic Intelligence Analysis Weblog





An illustration of the RvS coverage we be taught with simply supervised studying and a depth-two MLP. It makes use of no TD studying, benefit reweighting, or Transformers!

Offline reinforcement studying (RL) is conventionally approached utilizing value-based strategies based mostly on temporal distinction (TD) studying. Nevertheless, many current algorithms reframe RL as a supervised studying drawback. These algorithms be taught conditional insurance policies by conditioning on purpose states (Lynch et al., 2019; Ghosh et al., 2021), reward-to-go (Kumar et al., 2019; Chen et al., 2021), or language descriptions of the duty (Lynch and Sermanet, 2021).

We discover the simplicity of those strategies fairly interesting. If supervised studying is sufficient to resolve RL issues, then offline RL may turn into broadly accessible and (comparatively) straightforward to implement. Whereas TD studying should delicately steadiness an actor coverage with an ensemble of critics, these supervised studying strategies practice only one (conditional) coverage, and nothing else!

So, how can we use these strategies to successfully resolve offline RL issues? Prior work places ahead numerous intelligent suggestions and methods, however these methods are generally contradictory, making it difficult for practitioners to determine find out how to efficiently apply these strategies. For instance, RCPs (Kumar et al., 2019) require fastidiously reweighting the coaching information, GCSL (Ghosh et al., 2021) requires iterative, on-line information assortment, and Choice Transformer (Chen et al., 2021) makes use of a Transformer sequence mannequin because the coverage community.

Which, if any, of those hypotheses are appropriate? Do we have to reweight our coaching information based mostly on estimated benefits? Are Transformers essential to get a high-performing coverage? Are there different essential design choices which have been overlooked of prior work?

Our work goals to reply these questions by making an attempt to establish the important components of offline RL through supervised studying. We run experiments throughout 4 suites, 26 environments, and eight algorithms. When the mud settles, we get aggressive efficiency in each atmosphere suite we think about using remarkably easy components. The video above reveals the advanced habits we be taught utilizing simply supervised studying with a depth-two MLP – no TD studying, information reweighting, or Transformers!

Let’s start with an summary of the algorithm we research. Whereas plenty of prior work (Kumar et al., 2019; Ghosh et al., 2021; and Chen et al., 2021) share the identical core algorithm, it lacks a typical identify. To fill this hole, we suggest the time period RL through Supervised Studying (RvS). We’re not proposing any new algorithm however somewhat exhibiting how prior work will be considered from a unifying framework; see Determine 1.



Determine 1. (Left) A replay buffer of expertise (Proper) Hindsight relabelled coaching information

RL through Supervised Studying takes as enter a replay buffer of expertise together with states, actions, and outcomes. The outcomes will be an arbitrary operate of the trajectory, together with a purpose state, reward-to-go, or language description. Then, RvS performs hindsight relabeling to generate a dataset of state, motion, and consequence triplets. The instinct is that the actions which can be noticed present supervision for the outcomes which can be reached. With this coaching dataset, RvS performs supervised studying by maximizing the probability of the actions given the states and outcomes. This yields a conditional coverage that may situation on arbitrary outcomes at check time.

In our experiments, we give attention to the next three key questions.

  1. Which design choices are essential for RL through supervised studying?
  2. How properly does RL through supervised studying really work? We are able to do RL through supervised studying, however would utilizing a distinct offline RL algorithm carry out higher?
  3. What kind of consequence variable ought to we situation on? (And does it even matter?)



Determine 2. Our RvS structure. A depth-two MLP suffices in each atmosphere suite we think about.

We get good efficiency utilizing only a depth-two multi-layer perceptron. In reality, that is aggressive with all beforehand printed architectures we’re conscious of, together with a Transformer sequence mannequin. We simply concatenate the state and consequence earlier than passing them via two fully-connected layers (see Determine 2). The keys that we establish are having a community with giant capability – we use width 1024 – in addition to dropout in some environments. We discover that this works properly with out reweighting the coaching information or performing any further regularization.

After figuring out these key design choices, we research the general efficiency of RvS compared to earlier strategies. This weblog put up will overview outcomes from two of the suites we think about within the paper.


The primary suite is D4RL Fitness center, which accommodates the usual MuJoCo halfcheetah, hopper, and walker robots. The problem in D4RL Fitness center is to be taught locomotion insurance policies from offline datasets of various high quality. For instance, one offline dataset accommodates rollouts from a completely random coverage. One other dataset accommodates rollouts from a “medium” coverage educated partway to convergence, whereas one other dataset is a mix of rollouts from medium and skilled insurance policies.



Determine 3. Total efficiency in D4RL Fitness center.

Determine 3 reveals our leads to D4RL Fitness center. RvS-R is our implementation of RvS conditioned on rewards (illustrated in Determine 2). On common throughout all 12 duties within the suite, we see that RvS-R, which makes use of only a depth-two MLP, is aggressive with Choice Transformer (DT; Chen et al., 2021). We additionally see that RvS-R is aggressive with the strategies that use temporal distinction (TD) studying, together with CQL-R (Kumar et al., 2020), TD3+BC (Fujimoto et al., 2021), and Onestep (Brandfonbrener et al., 2021). Nevertheless, the TD studying strategies have an edge as a result of they carry out particularly properly on the random datasets. This implies that one would possibly choose TD studying over RvS when coping with low-quality information.


The second suite is D4RL AntMaze. This suite requires a quadruped to navigate to a goal location in mazes of various measurement. The problem of AntMaze is that many trajectories comprise solely items of the complete path from the begin to the purpose location. Studying from these trajectories requires stitching collectively these items to get the complete, profitable path.



Determine 4. Total efficiency in D4RL AntMaze.

Our AntMaze leads to Determine 4 spotlight the significance of the conditioning variable. Whereas conditioning RvS on rewards (RvS-R) was the only option of the conditioning variable in D4RL Fitness center, we discover that in D4RL AntMaze, it’s significantly better to situation RvS on $(x, y)$ purpose coordinates (RvS-G). Once we do that, we see that RvS-G compares favorably to TD studying! This was stunning to us as a result of TD studying explicitly performs dynamic programming utilizing the Bellman equation.

Why does goal-conditioning carry out higher than reward conditioning on this setting? Recall that AntMaze is designed so that straightforward imitation is just not sufficient: optimum strategies should sew collectively elements of suboptimal trajectories to determine find out how to attain the purpose. In precept, TD studying can resolve this with temporal compositionality. With the Bellman equation, TD studying can mix a path from A to B with a path from B to C, yielding a path from A to C. RvS-R, together with different habits cloning strategies, doesn’t profit from this temporal compositionality. We hypothesize that RvS-G, alternatively, advantages from spatial compositionality. It is because, in AntMaze, the coverage wanted to succeed in one purpose is just like the coverage wanted to succeed in a close-by purpose. We see correspondingly that RvS-G beats RvS-R.

In fact, conditioning RvS-G on $(x, y)$ coordinates represents a type of prior data in regards to the activity. However this additionally highlights an necessary consideration for RvS strategies: the selection of conditioning data is critically necessary, and it might rely considerably on the duty.

Total, we discover that in a various set of environments, RvS works properly while not having any fancy algorithmic methods (comparable to information reweighting) or fancy architectures (comparable to Transformers). Certainly, our easy RvS setup can match, and even outperform, strategies that make the most of (conservative) TD studying. The keys for RvS that we establish are mannequin capability, regularization, and the conditioning variable.

In our work, we handcraft the conditioning variable, comparable to $(x, y)$ coordinates in AntMaze. Past the usual offline RL setup, this introduces a further assumption, specifically, that we’ve some prior details about the construction of the duty. We predict an thrilling course for future work could be to take away this assumption by automating the educational of the purpose area.


We packaged our open-source code in order that it could possibly robotically deal with all of the dependencies for you. After downloading the code, you may run these 5 instructions to breed our experiments:

docker construct -t rvs:newest .
docker run -it --rm -v $(pwd):/rvs rvs:newest bash
cd rvs
pip set up -e .
bash experiments/launch_gym_rvs_r.sh

This put up is predicated on the paper:

RvS: What’s Important for Offline RL through Supervised Studying?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine
Worldwide Convention on Studying Representations (ICLR), 2022
[Paper] [Code]

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