Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and If you ran experiments... (a) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? Please refer to both main text and appendix for experiment details. Did you report error bars (e.g., with respect to the random seed after running experiments multiple All adaptation experiments in Procgen and RLBench are run for 3 seeds. Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal As stated in section 2, we use RTX A5000 GPUs each with 24GB memory. C2F-ARM algorithm and training framework are built based on the original author's implementation Did you mention the license of the assets?

First-order optimization methods are a foundational tool in machine learning.

Then,wecalculatethe mean stiffness of the value network across all state pairs and report its average computed over all trainingepochs. Eachagentis trained on 200 training levels for 25M environment steps. The mean and standard deviation are computedover10differentruns. Morespecifically,wecollect100 training episodes throughout the training and evaluate the value network prediction for the initial stateofeachtrajectory. Each agent is trained on 200 training levels for 25M environment steps.

We have not trained our models with sensitive or private data, and we emphasize that our model's direct L( n) other than the constant one as long as g (n) and l ( n) are positively correlated. The results for the baselines AdaSubS, kSubS, BC, CQL, DT, and HIPS with learned models were copied from [18]. The total number of GPU hours used on this work was approximately 7,500. We used 6 CPU workers (AMD Trento) per GPU. In the latter case, completeness cannot be guaranteed.

Agent 1's action is resolved first. Figure 8: An example of Agent 1 using the "clean" action while facing East. The "main" beam extends directly in front of the agent, while two auxiliary A beam stops when it hits a dirty river tile. The Sequential Social Dilemma Games, introduced in Leibo et al. [2017], are a kind of MARL All of these have open source implementations in [Vinitsky et al., 2019]. The cleaning beam is shown in Figure 8a.

Parameters5 10 3 5 10 3 5 10 3 (Critic Update:Environment Step) Ratio 1 1 1 (Policy Update:Environment Step) Ratio 2 2 1 Has Target Policy?