Model-based Reinforcement Learning
195 papers with code • 0 benchmarks • 1 datasets
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Libraries
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POPGym
Most implemented papers
Predictable MDP Abstraction for Unsupervised Model-Based RL
seohongpark/pma
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A key component of model-based reinforcement learning (RL) is a dynamics model that predicts the outcomes of actions.
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles.
Predictive Experience Replay for Continual Visual Control and Forecasting
jc043/cpl
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In this paper, we present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting.
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.
Deep Visual Foresight for Planning Robot Motion
m-serra/action-inference-for-video-prediction-benchmarking
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A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback.
Self-Correcting Models for Model-Based Reinforcement Learning
When an agent cannot represent a perfectly accurate model of its environment's dynamics, model-based reinforcement learning (MBRL) can fail catastrophically.
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks.
Safe Model-based Reinforcement Learning with Stability Guarantees
befelix/safe_learning
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NeurIPS 2017
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data.
Learning Approximate Stochastic Transition Models
YuhangSong/SGAN
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We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions.
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL.
