The model is trained to minimise the value function while still accurately predicting the transitions in the dataset, forcing the policy to act conservatively in areas not covered by the dataset. To approximately solve the two-player game, we alternate between optimising the policy and adversarially optimising the model.

Gym tasks are shown below in Table 8. Hyperparameter V alue Number of layers 3 Number of attention heads 1 Embedding dimension 128 Nonlinearity function ReLU Batch size 64 Context length K 20 HalfCheetah, Hopper, Walker 5 Reacher Return-to-go conditioning 6000 HalfCheetah 3600 Hopper 5000 Walker 50 Reacher Dropout 0 . 1 Learning rate 10 As briefly mentioned in Section 4.2, we found previously reported behavior cloning baselines to be The percentile behavior cloning experiments use the same hyperparameters. We give details of the illustrative example discussed in the introduction. The action is the integer index of the graph node to move to next. In this environment, we use the GPT model as described in Section 3 to generate both actions and return-to-go tokens.

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem.

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL "), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement

These results naturally pose the question of how such distributional estimators are learned and represented by neural systems.

We will also expand Section 2 to make it more gradual, stating theorems and definitions explicitly.

We consider settings where the receiver faces a sequential decision making (SDM) problem.