Learning how the world works is central to building AI agents that can adapt to complex environments. Traditional world models based on deep learning demand vast amounts of training data, and do not flexibly update their knowledge from sparse observations. Recent advances in program synthesis using Large Language Models (LLMs) give an alternate approach which learns world models represented as source code, supporting strong generalization from little data. To date, application of program-structured world models remains limited to natural language and grid-world domains. We introduce a novel program synthesis method for effectively modeling complex, non-gridworld domains by representing a world model as an exponentially-weighted product of programmatic experts (PoE-World) synthesized by LLMs. We show that this approach can learn complex, stochastic world models from just a few observations. We evaluate the learned world models by embedding them in a model-based planning agent, demonstrating efficient performance and generalization to unseen levels on Atari's Pong and Montezuma's Revenge.

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchicalDQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals.

In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward.

Deep reinforcement learning methods traditionally struggle withtaskswhere environment rewards are particularly sparse.

While this is not close to the state-of-the-art in general (c.f. Figure 2 shows the effect of action entropy on exploratory behavior in Montezuma's Revenge. Number of unique avatar positions visited. Full training curves across all 6 Atari games are shown in Figure 1, including the random policy baseline. To ensure this didn't hamper performance, we At each state visited by the agent evaluator during training, the agent's state (consisting of the avatar's The full curves are included for completeness. The compute cluster we performed experiments on is heterogenous, and has features such as host-sharing, adaptive load-balancing, etc.