Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Chua, Kurtland; Calandra, Roberto; McAllister, Rowan; Levine, Sergey

doi:10.48550/arxiv.1805.12114

Cited by 50 publications

(118 citation statements)

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“…State-based environments: We model the environment transition function using an neural ensemble of size N , where network's output neurons parameterize a Gaussian distribution T = N (µ(s t , a t ), Σ(s t , a t ) (Chua et al 2018).…”

Section: E2 Uncertainty Estimatorsmentioning

confidence: 99%

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Ma¹,

Shen²,

Bastani³

et al. 2021

Preprint

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Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reducing unsafe real-world actions: they may synthesize policies that obey all constraints using simulated samples from a learned model. However, imperfect models can result in realworld constraint violations even for actions that are predicted to satisfy all constraints. We propose Conservative and Adaptive Penalty (CAP), a model-based safe RL framework that accounts for potential modeling errors by capturing model uncertainty and adaptively exploiting it to balance the reward and the cost objectives. First, CAP inflates predicted costs using an uncertainty-based penalty. Theoretically, we show that policies that satisfy this conservative cost constraint are guaranteed to also be feasible in the true environment. We further show that this guarantees the safety of all intermediate solutions during RL training. Further, CAP adaptively tunes this penalty during training using true cost feedback from the environment. We evaluate this conservative and adaptive penalty-based approach for model-based safe RL extensively on state and image-based environments. Our results demonstrate substantial gains in sample-efficiency while incurring fewer violations than prior safe RL algorithms. Code is available at: https://github.com/Redrew/CAP

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Section: E2 Uncertainty Estimatorsmentioning

confidence: 99%

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Ma¹,

Shen²,

Bastani³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Model-based RL approaches typically alternate between fitting a predictive model of the environment dynamics/rewards and updating the control policies. The model can be used in various ways, such as execution-time planning [5,21], generating imaginary experiences for training the control policy [12,32]), etc. Our work is inspired by [7], which addresses the problem of error in long-horizon model dynamics prediction.…”

Section: Related Workmentioning

confidence: 99%

Multiagent Model-based Credit Assignment for Continuous Control

Han¹,

Lu²,

Michalak³

et al. 2021

Preprint

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Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability among all the components of a robot. However, agents in the real world often operate in a decentralised fashion without communication due to latency requirements, limited power budgets and safety concerns. By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control. To this end, we first develop a cooperative multiagent PPO framework that allows for centralized optimisation during training and decentralised operation during execution. However, the system only receives a global reward signal which is not attributed towards each agent. To address this challenge, we further propose a generic game-theoretic credit assignment framework which computes agent-specific reward signals. Last but not least, we also incorporate a model-based RL module into our credit assignment framework, which leads to significant improvement in sample efficiency. We demonstrate the effectiveness of our framework on experimental results on Mujoco locomotion control tasks. For a demo video please visit: https://youtu.be/gFyVPm4svEY.

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“…On the one hand stands the use of model predictive control in the engineering community where finely specified dynamics models are constructed by engineers and only a small number of parameters are fit with system identification to determine mass, inertia, joint stiffness, etc. On the other side of things stands the hands off approach taken in the RL community, where general and unstructured neural networks are used for both transition models [9,55,25] as well as policies and value functions [20]. The state and action spaces for these systems are highly complex with many diverse inputs like quaternions, joint angles, forces, torques that each transform in different ways under a symmetry transformation like a left-right reflection or a rotation.…”

Section: Approximate Symmetries In Reinforcement Learningmentioning

confidence: 99%

“…State of the art model based approaches on Mujoco tend to use an ensemble of small MLPs that predict the state transitions [9,55,25,2], without exploiting any structure of the state space. We evaluate test rollout predictions via the relative error of the state over different length horizons for the RPP model against an MLP, the method of choice.…”

Section: Better Transitionmentioning

confidence: 99%

Residual Pathway Priors for Soft Equivariance Constraints

Finzi¹,

Benton²,

Wilson³

2021

Preprint

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There is often a trade-off between building deep learning systems that are expressive enough to capture the nuances of the reality, and having the right inductive biases for efficient learning. We introduce Residual Pathway Priors (RPPs) as a method for converting hard architectural constraints into soft priors, guiding models towards structured solutions, while retaining the ability to capture additional complexity. Using RPPs, we construct neural network priors with inductive biases for equivariances, but without limiting flexibility. We show that RPPs are resilient to approximate or misspecified symmetries, and are as effective as fully constrained models even when symmetries are exact. We showcase the broad applicability of RPPs with dynamical systems, tabular data, and reinforcement learning. In Mujoco locomotion tasks, where contact forces and directional rewards violate strict equivariance assumptions, the RPP outperforms baseline model-free RL agents, and also improves the learned transition models for model-based RL.

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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Cited by 50 publications

References 0 publications

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Multiagent Model-based Credit Assignment for Continuous Control

Residual Pathway Priors for Soft Equivariance Constraints

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