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Goal-Conditioned Behavior Cloning (BC) is a promising approach for solving various offline reinforcement learning problems. Instead of assessing the value of states and actions, BC directly trains the Agent behavior policy, building dependencies between the set goal, the analyzed environment state and the Agent's action. This is achieved using supervised learning methods on pre-collected offline trajectories. The familiar Decision Transformer method and its derivative algorithms have demonstrated

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The approach to optimizing the Agent policy with constraints on its behavior turned out to be promising in solving offline reinforcement learning problems. By exploiting historical transitions, the Agent policy is trained to maximize a learned value function.

Behavior constrained policy can help to avoid a significant distribution shift in relation to Agent actions, which provides sufficient confidence in the assessment of the action costs. In the previous article we got acquainted

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