Model-based Reinforcement Learning
195 papers with code • 0 benchmarks • 1 datasets
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POPGym
Latest papers
Model-based Reinforcement Learning for Parameterized Action Spaces
valarzz/model-based-reinforcement-learning-for-parameterized-action-spaces
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We propose a novel model-based reinforcement learning algorithm -- Dynamics Learning and predictive control with Parameterized Actions (DLPA) -- for Parameterized Action Markov Decision Processes (PAMDPs).
Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards
yasinsonmez/symmetry-cs285
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Recent work in reinforcement learning has leveraged symmetries in the model to improve sample efficiency in training a policy.
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
We compare trajectory sampling with density-based approximation for uncertainty propagation using three different probabilistic world models; Gaussian processes, Bayesian neural networks, and DGCNs.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow.
Mastering Memory Tasks with World Models
chandar-lab/Recall2Imagine
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Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze.
Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
We found that the high dimensionality of the thermal dynamics model input hinders the efficiency of policy extraction.
Model-based deep reinforcement learning for accelerated learning from flow simulations
janisgeise/mb_drl_for_accelerated_learning_from_cfd
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Employing simulation-based environments in reinforcement learning enables a priori end-to-end optimization of the control system, provides a virtual testbed for safety-critical control applications, and allows to gain a deep understanding of the control mechanisms.
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
The prevailing theoretical understanding is that this can then be viewed as online reinforcement learning in an approximate dynamics model, and any remaining gap is therefore assumed to be due to the imperfect dynamics model.
A Distributional Analogue to the Successor Representation
jessefarebro/distributional-sr
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This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process.
Augmenting Replay in World Models for Continual Reinforcement Learning
cerenaut/wmar
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Also, the concept of replay comes from biological inspiration, where evidence suggests that replay is applied to a world model, which implies model-based RL -- and model-based RL should have benefits for continual RL, where it is possible to exploit knowledge independent of the policy.
